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diff --git a/README.md b/README.md index 575a6a1a..e74c91e5 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Release version: [3.14c](https://github.com/AFLplusplus/AFLplusplus/releases) GitHub version: 3.15a -Repository: +Repository: [https://github.com/AFLplusplus/AFLplusplus](https://github.com/AFLplusplus/AFLplusplus) AFL++ is maintained by: @@ -18,33 +18,33 @@ AFL++ is maintained by: Originally developed by Michał "lcamtuf" Zalewski. -AFL++ is a superior fork to Google's AFL - more speed, more and better +AFL++ is a superior fork to Google's AFL - more speed, more and better mutations, more and better instrumentation, custom module support, etc. -You are free to copy, modify, and distribute AFL++ with attribution under the +You are free to copy, modify, and distribute AFL++ with attribution under the terms of the Apache-2.0 License. See the [LICENSE](LICENSE) for details. ## Getting started Here is some information to get you started: -* For releases, please see the - [Releases](https://github.com/AFLplusplus/AFLplusplus/releases) tab and - [branches](docs/branches.md). Also take a look at the list of +* For releases, please see the + [Releases tab](https://github.com/AFLplusplus/AFLplusplus/releases) and + [branches](#branches). Also take a look at the list of [important changes in AFL++](docs/important_changes.md). -* If you want to use AFL++ for your academic work, check the +* If you want to use AFL++ for your academic work, check the [papers page](https://aflplus.plus/papers/) on the website. * To cite our work, look at the [Cite](#cite) section. -* For comparisons, use the fuzzbench `aflplusplus` setup, or use - `afl-clang-fast` with `AFL_LLVM_CMPLOG=1`. You can find the `aflplusplus` - default configuration on Google's +* For comparisons, use the fuzzbench `aflplusplus` setup, or use + `afl-clang-fast` with `AFL_LLVM_CMPLOG=1`. You can find the `aflplusplus` + default configuration on Google's [fuzzbench](https://github.com/google/fuzzbench/tree/master/fuzzers/aflplusplus). -* To get you started with tutorials, go to +* To get you started with tutorials, go to [docs/tutorials.md](docs/tutorials.md). ## Building and installing AFL++ -To have AFL++ easily available with everything compiled, pull the image +To have AFL++ easily available with everything compiled, pull the image directly from the Docker Hub: ```shell @@ -52,95 +52,110 @@ docker pull aflplusplus/aflplusplus docker run -ti -v /location/of/your/target:/src aflplusplus/aflplusplus ``` -This image is automatically generated when a push to the stable repo happens -(see [docs/branches.md](docs/branches.md)). You will find your target source +This image is automatically generated when a push to the stable repo happens +(see [branches](#branches)). You will find your target source code in `/src` in the container. To build AFL++ yourself, continue at [docs/INSTALL.md](docs/INSTALL.md). ## Quick start: Fuzzing with AFL++ -*NOTE: Before you start, please read about the [common sense risks of -fuzzing](docs/common_sense_risks.md).* +*NOTE: Before you start, please read about the +[common sense risks of fuzzing](docs/fuzzing_in_depth.md#0-common-sense-risks).* -This is a quick start for fuzzing targets with the source code available. To +This is a quick start for fuzzing targets with the source code available. To read about the process in detail, see -[docs/fuzzing_expert.md](docs/fuzzing_expert.md). +[docs/fuzzing_in_depth.md](docs/fuzzing_in_depth.md). To learn about fuzzing other targets, see: -* Binary-only targets: +* Binary-only targets: [docs/fuzzing_binary-only_targets.md](docs/fuzzing_binary-only_targets.md) -* Network services: +* Network services: [docs/best_practices.md#fuzzing-a-network-service](docs/best_practices.md#fuzzing-a-network-service) -* GUI programs: +* GUI programs: [docs/best_practices.md#fuzzing-a-gui-program](docs/best_practices.md#fuzzing-a-gui-program) Step-by-step quick start: -1. Compile the program or library to be fuzzed using `afl-cc`. A common way to +1. Compile the program or library to be fuzzed using `afl-cc`. A common way to do this would be: - CC=/path/to/afl-cc CXX=/path/to/afl-c++ ./configure --disable-shared - make clean all + ``` + CC=/path/to/afl-cc CXX=/path/to/afl-c++ ./configure --disable-shared + make clean all + ``` -2. Get a small but valid input file that makes sense to the program. When - fuzzing verbose syntax (SQL, HTTP, etc), create a dictionary as described in +2. Get a small but valid input file that makes sense to the program. When + fuzzing verbose syntax (SQL, HTTP, etc), create a dictionary as described in [dictionaries/README.md](dictionaries/README.md), too. 3. If the program reads from stdin, run `afl-fuzz` like so: -``` + ``` ./afl-fuzz -i seeds_dir -o output_dir -- \ - /path/to/tested/program [...program's cmdline...] -``` + /path/to/tested/program [...program's cmdline...] + ``` To add a dictionary, add `-x /path/to/dictionary.txt` to afl-fuzz. - If the program takes input from a file, you can put `@@` in the program's + If the program takes input from a file, you can put `@@` in the program's command line; AFL will put an auto-generated file name in there for you. -4. Investigate anything shown in red in the fuzzer UI by promptly consulting - [docs/status_screen.md](docs/status_screen.md). +4. Investigate anything shown in red in the fuzzer UI by promptly consulting + [docs/afl-fuzz_approach.md#understanding-the-status-screen](docs/afl-fuzz_approach.md#understanding-the-status-screen). + +5. You will find found crashes and hangs in the subdirectories `crashes/` and + `hangs/` in the `-o output_dir` directory. You can replay the crashes by + feeding them to the target, e.g.: + + ``` + cat output_dir/crashes/id:000000,* | /path/to/tested/program [...program's cmdline...] + ``` -5. You will find found crashes and hangs in the subdirectories `crashes/` and - `hangs/` in the `-o output_dir` directory. You can replay the crashes by - feeding them to the target, e.g.: `cat output_dir/crashes/id:000000,* | - /path/to/tested/program [...program's cmdline...]` You can generate cores or - use gdb directly to follow up the crashes. + You can generate cores or use gdb directly to follow up the crashes. ## Contact Questions? Concerns? Bug reports? -* The contributors can be reached via +* The contributors can be reached via [https://github.com/AFLplusplus/AFLplusplus](https://github.com/AFLplusplus/AFLplusplus). -* Take a look at our [FAQ](docs/FAQ.md). If you find an interesting or - important question missing, submit it via +* Take a look at our [FAQ](docs/FAQ.md). If you find an interesting or + important question missing, submit it via [https://github.com/AFLplusplus/AFLplusplus/discussions](https://github.com/AFLplusplus/AFLplusplus/discussions). -* There is a mailing list for the AFL/AFL++ project - ([browse archive](https://groups.google.com/group/afl-users)). To compare - notes with other users or to get notified about major new features, send an +* There is a mailing list for the AFL/AFL++ project + ([browse archive](https://groups.google.com/group/afl-users)). To compare + notes with other users or to get notified about major new features, send an email to <afl-users+subscribe@googlegroups.com>. * Or join the [Awesome Fuzzing](https://discord.gg/gCraWct) Discord server. +## Branches + +The following branches exist: + +* [release](https://github.com/AFLplusplus/AFLplusplus/tree/release): the latest release +* [stable/trunk](https://github.com/AFLplusplus/AFLplusplus/): stable state of AFL++ - it is synced from dev from time to time when we are satisfied with its stability +* [dev](https://github.com/AFLplusplus/AFLplusplus/tree/dev): development state of AFL++ - bleeding edge and you might catch a checkout which does not compile or has a bug. *We only accept PRs in dev!!* +* (any other): experimental branches to work on specific features or testing new functionality or changes. + ## Help wanted -We have several [ideas](docs/ideas.md) we would like to see in AFL++ to make it -even better. However, we already work on so many things that we do not have the +We have several [ideas](docs/ideas.md) we would like to see in AFL++ to make it +even better. However, we already work on so many things that we do not have the time for all the big ideas. -This can be your way to support and contribute to AFL++ - extend it to do +This can be your way to support and contribute to AFL++ - extend it to do something cool. -For everyone who wants to contribute (and send pull requests), please read our +For everyone who wants to contribute (and send pull requests), please read our [contributing guidelines](CONTRIBUTING.md) before your submit. ## Special thanks -Many of the improvements to the original AFL and AFL++ wouldn't be possible +Many of the improvements to the original AFL and AFL++ wouldn't be possible without feedback, bug reports, or patches from our contributors. -Thank you! (For people sending pull requests - please add yourself to this list +Thank you! (For people sending pull requests - please add yourself to this list :-) <details> @@ -200,8 +215,8 @@ Thank you! (For people sending pull requests - please add yourself to this list ## Cite -If you use AFL++ in scientific work, consider citing -[our paper](https://www.usenix.org/conference/woot20/presentation/fioraldi) +If you use AFL++ in scientific work, consider citing +[our paper](https://www.usenix.org/conference/woot20/presentation/fioraldi) presented at WOOT'20: Andrea Fioraldi, Dominik Maier, Heiko Eißfeldt, and Marc Heuse. “AFL++: Combining incremental steps of fuzzing research”. In 14th USENIX Workshop on Offensive Technologies (WOOT 20). USENIX Association, Aug. 2020. @@ -221,4 +236,4 @@ presented at WOOT'20: } ``` -</details> +</details> \ No newline at end of file diff --git a/docs/FAQ.md b/docs/FAQ.md index 68ca3bad..34ed4cf5 100644 --- a/docs/FAQ.md +++ b/docs/FAQ.md @@ -83,7 +83,8 @@ If you find an interesting or important question missing, submit it via However, if there is only the binary program and no source code available, then the standard non-instrumented mode is not effective. - To learn how these binaries can be fuzzed, read [binaryonly_fuzzing.md](binaryonly_fuzzing.md). + To learn how these binaries can be fuzzed, read + [fuzzing_binary-only_targets.md](fuzzing_binary-only_targets.md). </p></details> <details> @@ -143,7 +144,7 @@ If you find an interesting or important question missing, submit it via Target: x86_64-unknown-linux-gnu Thread model: posix InstalledDir: /prg/tmp/llvm-project/build/bin - clang-13: note: diagnostic msg: + clang-13: note: diagnostic msg: ******************** ``` diff --git a/docs/afl-fuzz_approach.md b/docs/afl-fuzz_approach.md index 5652816b..e0d5a1c9 100644 --- a/docs/afl-fuzz_approach.md +++ b/docs/afl-fuzz_approach.md @@ -1,37 +1,541 @@ # The afl-fuzz approach -American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple -but rock-solid instrumentation-guided genetic algorithm. It uses a modified -form of edge coverage to effortlessly pick up subtle, local-scale changes to -program control flow. +AFL++ is a brute-force fuzzer coupled with an exceedingly simple but rock-solid +instrumentation-guided genetic algorithm. It uses a modified form of edge +coverage to effortlessly pick up subtle, local-scale changes to program control +flow. Simplifying a bit, the overall algorithm can be summed up as: - 1) Load user-supplied initial test cases into the queue, +1) Load user-supplied initial test cases into the queue. - 2) Take the next input file from the queue, +2) Take the next input file from the queue. - 3) Attempt to trim the test case to the smallest size that doesn't alter - the measured behavior of the program, +3) Attempt to trim the test case to the smallest size that doesn't alter the + measured behavior of the program. - 4) Repeatedly mutate the file using a balanced and well-researched variety - of traditional fuzzing strategies, +4) Repeatedly mutate the file using a balanced and well-researched variety of + traditional fuzzing strategies. - 5) If any of the generated mutations resulted in a new state transition - recorded by the instrumentation, add mutated output as a new entry in the - queue. +5) If any of the generated mutations resulted in a new state transition recorded + by the instrumentation, add mutated output as a new entry in the queue. - 6) Go to 2. +6) Go to 2. The discovered test cases are also periodically culled to eliminate ones that have been obsoleted by newer, higher-coverage finds; and undergo several other instrumentation-driven effort minimization steps. As a side result of the fuzzing process, the tool creates a small, -self-contained corpus of interesting test cases. These are extremely useful -for seeding other, labor- or resource-intensive testing regimes - for example, -for stress-testing browsers, office applications, graphics suites, or -closed-source tools. +self-contained corpus of interesting test cases. These are extremely useful for +seeding other, labor- or resource-intensive testing regimes - for example, for +stress-testing browsers, office applications, graphics suites, or closed-source +tools. The fuzzer is thoroughly tested to deliver out-of-the-box performance far -superior to blind fuzzing or coverage-only tools. \ No newline at end of file +superior to blind fuzzing or coverage-only tools. + +## Understanding the status screen + +This chapter provides an overview of the status screen - plus tips for +troubleshooting any warnings and red text shown in the UI. + +For the general instruction manual, see [README.md](../README.md). + +### A note about colors + +The status screen and error messages use colors to keep things readable and +attract your attention to the most important details. For example, red almost +always means "consult this doc" :-) + +Unfortunately, the UI will only render correctly if your terminal is using +traditional un*x palette (white text on black background) or something close to +that. + +If you are using inverse video, you may want to change your settings, say: + +- For GNOME Terminal, go to `Edit > Profile` preferences, select the "colors" + tab, and from the list of built-in schemes, choose "white on black". +- For the MacOS X Terminal app, open a new window using the "Pro" scheme via the + `Shell > New Window` menu (or make "Pro" your default). + +Alternatively, if you really like your current colors, you can edit config.h to +comment out USE_COLORS, then do `make clean all`. + +We are not aware of any other simple way to make this work without causing other +side effects - sorry about that. + +With that out of the way, let's talk about what's actually on the screen... + +### The status bar + +``` +american fuzzy lop ++3.01a (default) [fast] {0} +``` + +The top line shows you which mode afl-fuzz is running in (normal: "american +fuzzy lop", crash exploration mode: "peruvian rabbit mode") and the version of +AFL++. Next to the version is the banner, which, if not set with -T by hand, +will either show the binary name being fuzzed, or the -M/-S main/secondary name +for parallel fuzzing. Second to last is the power schedule mode being run +(default: fast). Finally, the last item is the CPU id. + +### Process timing + +``` + +----------------------------------------------------+ + | run time : 0 days, 8 hrs, 32 min, 43 sec | + | last new path : 0 days, 0 hrs, 6 min, 40 sec | + | last uniq crash : none seen yet | + | last uniq hang : 0 days, 1 hrs, 24 min, 32 sec | + +----------------------------------------------------+ +``` + +This section is fairly self-explanatory: it tells you how long the fuzzer has +been running and how much time has elapsed since its most recent finds. This is +broken down into "paths" (a shorthand for test cases that trigger new execution +patterns), crashes, and hangs. + +When it comes to timing: there is no hard rule, but most fuzzing jobs should be +expected to run for days or weeks; in fact, for a moderately complex project, +the first pass will probably take a day or so. Every now and then, some jobs +will be allowed to run for months. + +There's one important thing to watch out for: if the tool is not finding new +paths within several minutes of starting, you're probably not invoking the +target binary correctly and it never gets to parse the input files we're +throwing at it; other possible explanations are that the default memory limit +(`-m`) is too restrictive and the program exits after failing to allocate a +buffer very early on; or that the input files are patently invalid and always +fail a basic header check. + +If there are no new paths showing up for a while, you will eventually see a big +red warning in this section, too :-) + +### Overall results + +``` + +-----------------------+ + | cycles done : 0 | + | total paths : 2095 | + | uniq crashes : 0 | + | uniq hangs : 19 | + +-----------------------+ +``` + +The first field in this section gives you the count of queue passes done so far +- that is, the number of times the fuzzer went over all the interesting test + cases discovered so far, fuzzed them, and looped back to the very beginning. + Every fuzzing session should be allowed to complete at least one cycle; and + ideally, should run much longer than that. + +As noted earlier, the first pass can take a day or longer, so sit back and +relax. + +To help make the call on when to hit `Ctrl-C`, the cycle counter is color-coded. +It is shown in magenta during the first pass, progresses to yellow if new finds +are still being made in subsequent rounds, then blue when that ends - and +finally, turns green after the fuzzer hasn't been seeing any action for a longer +while. + +The remaining fields in this part of the screen should be pretty obvious: +there's the number of test cases ("paths") discovered so far, and the number of +unique faults. The test cases, crashes, and hangs can be explored in real-time +by browsing the output directory, see +[#interpreting-output](#interpreting-output). + +### Cycle progress + +``` + +-------------------------------------+ + | now processing : 1296 (61.86%) | + | paths timed out : 0 (0.00%) | + +-------------------------------------+ +``` + +This box tells you how far along the fuzzer is with the current queue cycle: it +shows the ID of the test case it is currently working on, plus the number of +inputs it decided to ditch because they were persistently timing out. + +The "*" suffix sometimes shown in the first line means that the currently +processed path is not "favored" (a property discussed later on). + +### Map coverage + +``` + +--------------------------------------+ + | map density : 10.15% / 29.07% | + | count coverage : 4.03 bits/tuple | + +--------------------------------------+ +``` + +The section provides some trivia about the coverage observed by the +instrumentation embedded in the target binary. + +The first line in the box tells you how many branch tuples we have already hit, +in proportion to how much the bitmap can hold. The number on the left describes +the current input; the one on the right is the value for the entire input +corpus. + +Be wary of extremes: + +- Absolute numbers below 200 or so suggest one of three things: that the program + is extremely simple; that it is not instrumented properly (e.g., due to being + linked against a non-instrumented copy of the target library); or that it is + bailing out prematurely on your input test cases. The fuzzer will try to mark + this in pink, just to make you aware. +- Percentages over 70% may very rarely happen with very complex programs that + make heavy use of template-generated code. Because high bitmap density makes + it harder for the fuzzer to reliably discern new program states, we recommend + recompiling the binary with `AFL_INST_RATIO=10` or so and trying again (see + [env_variables.md](env_variables.md)). The fuzzer will flag high percentages + in red. Chances are, you will never see that unless you're fuzzing extremely + hairy software (say, v8, perl, ffmpeg). + +The other line deals with the variability in tuple hit counts seen in the +binary. In essence, if every taken branch is always taken a fixed number of +times for all the inputs we have tried, this will read `1.00`. As we manage to +trigger other hit counts for every branch, the needle will start to move toward +`8.00` (every bit in the 8-bit map hit), but will probably never reach that +extreme. + +Together, the values can be useful for comparing the coverage of several +different fuzzing jobs that rely on the same instrumented binary. + +### Stage progress + +``` + +-------------------------------------+ + | now trying : interest 32/8 | + | stage execs : 3996/34.4k (11.62%) | + | total execs : 27.4M | + | exec speed : 891.7/sec | + +-------------------------------------+ +``` + +This part gives you an in-depth peek at what the fuzzer is actually doing right +now. It tells you about the current stage, which can be any of: + +- calibration - a pre-fuzzing stage where the execution path is examined to + detect anomalies, establish baseline execution speed, and so on. Executed very + briefly whenever a new find is being made. +- trim L/S - another pre-fuzzing stage where the test case is trimmed to the + shortest form that still produces the same execution path. The length (L) and + stepover (S) are chosen in general relationship to file size. +- bitflip L/S - deterministic bit flips. There are L bits toggled at any given + time, walking the input file with S-bit increments. The current L/S variants + are: `1/1`, `2/1`, `4/1`, `8/8`, `16/8`, `32/8`. +- arith L/8 - deterministic arithmetics. The fuzzer tries to subtract or add + small integers to 8-, 16-, and 32-bit values. The stepover is always 8 bits. +- interest L/8 - deterministic value overwrite. The fuzzer has a list of known + "interesting" 8-, 16-, and 32-bit values to try. The stepover is 8 bits. +- extras - deterministic injection of dictionary terms. This can be shown as + "user" or "auto", depending on whether the fuzzer is using a user-supplied + dictionary (`-x`) or an auto-created one. You will also see "over" or + "insert", depending on whether the dictionary words overwrite existing data or + are inserted by offsetting the remaining data to accommodate their length. +- havoc - a sort-of-fixed-length cycle with stacked random tweaks. The + operations attempted during this stage include bit flips, overwrites with + random and "interesting" integers, block deletion, block duplication, plus + assorted dictionary-related operations (if a dictionary is supplied in the + first place). +- splice - a last-resort strategy that kicks in after the first full queue cycle + with no new paths. It is equivalent to 'havoc', except that it first splices + together two random inputs from the queue at some arbitrarily selected + midpoint. +- sync - a stage used only when `-M` or `-S` is set (see + [parallel_fuzzing.md](parallel_fuzzing.md)). No real fuzzing is involved, but + the tool scans the output from other fuzzers and imports test cases as + necessary. The first time this is done, it may take several minutes or so. + +The remaining fields should be fairly self-evident: there's the exec count +progress indicator for the current stage, a global exec counter, and a benchmark +for the current program execution speed. This may fluctuate from one test case +to another, but the benchmark should be ideally over 500 execs/sec most of the +time - and if it stays below 100, the job will probably take very long. + +The fuzzer will explicitly warn you about slow targets, too. If this happens, +see the [perf_tips.md](perf_tips.md) file included with the fuzzer for ideas on +how to speed things up. + +### Findings in depth + +``` + +--------------------------------------+ + | favored paths : 879 (41.96%) | + | new edges on : 423 (20.19%) | + | total crashes : 0 (0 unique) | + | total tmouts : 24 (19 unique) | + +--------------------------------------+ +``` + +This gives you several metrics that are of interest mostly to complete nerds. +The section includes the number of paths that the fuzzer likes the most based on +a minimization algorithm baked into the code (these will get considerably more +air time), and the number of test cases that actually resulted in better edge +coverage (versus just pushing the branch hit counters up). There are also +additional, more detailed counters for crashes and timeouts. + +Note that the timeout counter is somewhat different from the hang counter; this +one includes all test cases that exceeded the timeout, even if they did not +exceed it by a margin sufficient to be classified as hangs. + +### Fuzzing strategy yields + +``` + +-----------------------------------------------------+ + | bit flips : 57/289k, 18/289k, 18/288k | + | byte flips : 0/36.2k, 4/35.7k, 7/34.6k | + | arithmetics : 53/2.54M, 0/537k, 0/55.2k | + | known ints : 8/322k, 12/1.32M, 10/1.70M | + | dictionary : 9/52k, 1/53k, 1/24k | + |havoc/splice : 1903/20.0M, 0/0 | + |py/custom/rq : unused, 53/2.54M, unused | + | trim/eff : 20.31%/9201, 17.05% | + +-----------------------------------------------------+ +``` + +This is just another nerd-targeted section keeping track of how many paths we +have netted, in proportion to the number of execs attempted, for each of the +fuzzing strategies discussed earlier on. This serves to convincingly validate +assumptions about the usefulness of the various approaches taken by afl-fuzz. + +The trim strategy stats in this section are a bit different than the rest. The +first number in this line shows the ratio of bytes removed from the input files; +the second one corresponds to the number of execs needed to achieve this goal. +Finally, the third number shows the proportion of bytes that, although not +possible to remove, were deemed to have no effect and were excluded from some of +the more expensive deterministic fuzzing steps. + +Note that when deterministic mutation mode is off (which is the default because +it is not very efficient) the first five lines display "disabled (default, +enable with -D)". + +Only what is activated will have counter shown. + +### Path geometry + +``` + +---------------------+ + | levels : 5 | + | pending : 1570 | + | pend fav : 583 | + | own finds : 0 | + | imported : 0 | + | stability : 100.00% | + +---------------------+ +``` + +The first field in this section tracks the path depth reached through the guided +fuzzing process. In essence: the initial test cases supplied by the user are +considered "level 1". The test cases that can be derived from that through +traditional fuzzing are considered "level 2"; the ones derived by using these as +inputs to subsequent fuzzing rounds are "level 3"; and so forth. The maximum +depth is therefore a rough proxy for how much value you're getting out of the +instrumentation-guided approach taken by afl-fuzz. + +The next field shows you the number of inputs that have not gone through any +fuzzing yet. The same stat is also given for "favored" entries that the fuzzer +really wants to get to in this queue cycle (the non-favored entries may have to +wait a couple of cycles to get their chance). + +Next, we have the number of new paths found during this fuzzing section and +imported from other fuzzer instances when doing parallelized fuzzing; and the +extent to which identical inputs appear to sometimes produce variable behavior +in the tested binary. + +That last bit is actually fairly interesting: it measures the consistency of +observed traces. If a program always behaves the same for the same input data, +it will earn a score of 100%. When the value is lower but still shown in purple, +the fuzzing process is unlikely to be negatively affected. If it goes into red, +you may be in trouble, since AFL will have difficulty discerning between +meaningful and "phantom" effects of tweaking the input file. + +Now, most targets will just get a 100% score, but when you see lower figures, +there are several things to look at: + +- The use of uninitialized memory in conjunction with some intrinsic sources of + entropy in the tested binary. Harmless to AFL, but could be indicative of a + security bug. +- Attempts to manipulate persistent resources, such as left over temporary files + or shared memory objects. This is usually harmless, but you may want to + double-check to make sure the program isn't bailing out prematurely. Running + out of disk space, SHM handles, or other global resources can trigger this, + too. +- Hitting some functionality that is actually designed to behave randomly. + Generally harmless. For example, when fuzzing sqlite, an input like `select + random();` will trigger a variable execution path. +- Multiple threads executing at once in semi-random order. This is harmless when + the 'stability' metric stays over 90% or so, but can become an issue if not. + Here's what to try: + * Use afl-clang-fast from [instrumentation](../instrumentation/) - it uses a + thread-local tracking model that is less prone to concurrency issues, + * See if the target can be compiled or run without threads. Common + `./configure` options include `--without-threads`, `--disable-pthreads`, or + `--disable-openmp`. + * Replace pthreads with GNU Pth (https://www.gnu.org/software/pth/), which + allows you to use a deterministic scheduler. +- In persistent mode, minor drops in the "stability" metric can be normal, + because not all the code behaves identically when re-entered; but major dips + may signify that the code within `__AFL_LOOP()` is not behaving correctly on + subsequent iterations (e.g., due to incomplete clean-up or reinitialization of + the state) and that most of the fuzzing effort goes to waste. + +The paths where variable behavior is detected are marked with a matching entry +in the `<out_dir>/queue/.state/variable_behavior/` directory, so you can look +them up easily. + +### CPU load + +``` + [cpu: 25%] +``` + +This tiny widget shows the apparent CPU utilization on the local system. It is +calculated by taking the number of processes in the "runnable" state, and then +comparing it to the number of logical cores on the system. + +If the value is shown in green, you are using fewer CPU cores than available on +your system and can probably parallelize to improve performance; for tips on how +to do that, see [parallel_fuzzing.md](parallel_fuzzing.md). + +If the value is shown in red, your CPU is *possibly* oversubscribed, and running +additional fuzzers may not give you any benefits. + +Of course, this benchmark is very simplistic; it tells you how many processes +are ready to run, but not how resource-hungry they may be. It also doesn't +distinguish between physical cores, logical cores, and virtualized CPUs; the +performance characteristics of each of these will differ quite a bit. + +If you want a more accurate measurement, you can run the `afl-gotcpu` utility +from the command line. + +## Interpreting output + +See [#understanding-the-status-screen](#understanding-the-status-screen) for +information on how to interpret the displayed stats and monitor the health of +the process. Be sure to consult this file especially if any UI elements are +highlighted in red. + +The fuzzing process will continue until you press Ctrl-C. At a minimum, you want +to allow the fuzzer to complete one queue cycle, which may take anywhere from a +couple of hours to a week or so. + +There are three subdirectories created within the output directory and updated +in real-time: + +- queue/ - test cases for every distinctive execution path, plus all the + starting files given by the user. This is the synthesized corpus + mentioned in section 2. + + Before using this corpus for any other purposes, you can shrink + it to a smaller size using the afl-cmin tool. The tool will find + a smaller subset of files offering equivalent edge coverage. + +- crashes/ - unique test cases that cause the tested program to receive a fatal + signal (e.g., SIGSEGV, SIGILL, SIGABRT). The entries are grouped by + the received signal. + +- hangs/ - unique test cases that cause the tested program to time out. The + default time limit before something is classified as a hang is the + larger of 1 second and the value of the -t parameter. The value can + be fine-tuned by setting AFL_HANG_TMOUT, but this is rarely + necessary. + +Crashes and hangs are considered "unique" if the associated execution paths +involve any state transitions not seen in previously-recorded faults. If a +single bug can be reached in multiple ways, there will be some count inflation +early in the process, but this should quickly taper off. + +The file names for crashes and hangs are correlated with the parent, non-faulting +queue entries. This should help with debugging. + +## Visualizing + +If you have gnuplot installed, you can also generate some pretty graphs for any +active fuzzing task using afl-plot. For an example of how this looks like, see +[https://lcamtuf.coredump.cx/afl/plot/](https://lcamtuf.coredump.cx/afl/plot/). + +You can also manually build and install afl-plot-ui, which is a helper utility +for showing the graphs generated by afl-plot in a graphical window using GTK. +You can build and install it as follows: + +```shell +sudo apt install libgtk-3-0 libgtk-3-dev pkg-config +cd utils/plot_ui +make +cd ../../ +sudo make install +``` + + +### Addendum: status and plot files + +For unattended operation, some of the key status screen information can be also +found in a machine-readable format in the fuzzer_stats file in the output +directory. This includes: + +- `start_time` - unix time indicating the start time of afl-fuzz +- `last_update` - unix time corresponding to the last update of this file +- `run_time` - run time in seconds to the last update of this file +- `fuzzer_pid` - PID of the fuzzer process +- `cycles_done` - queue cycles completed so far +- `cycles_wo_finds` - number of cycles without any new paths found +- `execs_done` - number of execve() calls attempted +- `execs_per_sec` - overall number of execs per second +- `paths_total` - total number of entries in the queue +- `paths_favored` - number of queue entries that are favored +- `paths_found` - number of entries discovered through local fuzzing +- `paths_imported` - number of entries imported from other instances +- `max_depth` - number of levels in the generated data set +- `cur_path` - currently processed entry number +- `pending_favs` - number of favored entries still waiting to be fuzzed +- `pending_total` - number of all entries waiting to be fuzzed +- `variable_paths` - number of test cases showing variable behavior +- `stability` - percentage of bitmap bytes that behave consistently +- `bitmap_cvg` - percentage of edge coverage found in the map so far +- `unique_crashes` - number of unique crashes recorded +- `unique_hangs` - number of unique hangs encountered +- `last_path` - seconds since the last path was found +- `last_crash` - seconds since the last crash was found +- `last_hang` - seconds since the last hang was found +- `execs_since_crash` - execs since the last crash was found +- `exec_timeout` - the -t command line value +- `slowest_exec_ms` - real time of the slowest execution in ms +- `peak_rss_mb` - max rss usage reached during fuzzing in MB +- `edges_found` - how many edges have been found +- `var_byte_count` - how many edges are non-deterministic +- `afl_banner` - banner text (e.g. the target name) +- `afl_version` - the version of AFL used +- `target_mode` - default, persistent, qemu, unicorn, non-instrumented +- `command_line` - full command line used for the fuzzing session + +Most of these map directly to the UI elements discussed earlier on. + +On top of that, you can also find an entry called `plot_data`, containing a +plottable history for most of these fields. If you have gnuplot installed, you +can turn this into a nice progress report with the included `afl-plot` tool. + +### Addendum: automatically sending metrics with StatsD + +In a CI environment or when running multiple fuzzers, it can be tedious to log +into each of them or deploy scripts to read the fuzzer statistics. Using +`AFL_STATSD` (and the other related environment variables `AFL_STATSD_HOST`, +`AFL_STATSD_PORT`, `AFL_STATSD_TAGS_FLAVOR`) you can automatically send metrics +to your favorite StatsD server. Depending on your StatsD server, you will be +able to monitor, trigger alerts, or perform actions based on these metrics (e.g: +alert on slow exec/s for a new build, threshold of crashes, time since last +crash > X, etc). + +The selected metrics are a subset of all the metrics found in the status and in +the plot file. The list is the following: `cycle_done`, `cycles_wo_finds`, +`execs_done`,`execs_per_sec`, `paths_total`, `paths_favored`, `paths_found`, +`paths_imported`, `max_depth`, `cur_path`, `pending_favs`, `pending_total`, +`variable_paths`, `unique_crashes`, `unique_hangs`, `total_crashes`, +`slowest_exec_ms`, `edges_found`, `var_byte_count`, `havoc_expansion`. Their +definitions can be found in the addendum above. + +When using multiple fuzzer instances with StatsD, it is *strongly* recommended +to setup the flavor (AFL_STATSD_TAGS_FLAVOR) to match your StatsD server. This +will allow you to see individual fuzzer performance, detect bad ones, see the +progress of each strategy... \ No newline at end of file diff --git a/docs/best_practices.md b/docs/best_practices.md index 5d07dd14..979849f4 100644 --- a/docs/best_practices.md +++ b/docs/best_practices.md @@ -4,20 +4,26 @@ ### Targets - * [Fuzzing a binary-only target](#fuzzing-a-binary-only-target) - * [Fuzzing a GUI program](#fuzzing-a-gui-program) - * [Fuzzing a network service](#fuzzing-a-network-service) +* [Fuzzing a target with source code available](#fuzzing-a-target-with-source-code-available) +* [Fuzzing a binary-only target](#fuzzing-a-binary-only-target) +* [Fuzzing a GUI program](#fuzzing-a-gui-program) +* [Fuzzing a network service](#fuzzing-a-network-service) ### Improvements - * [Improving speed](#improving-speed) - * [Improving stability](#improving-stability) +* [Improving speed](#improving-speed) +* [Improving stability](#improving-stability) ## Targets +### Fuzzing a target with source code available + +To learn how to fuzz a target if source code is available, see [fuzzing_in_depth.md](fuzzing_in_depth.md). + ### Fuzzing a binary-only target -For a comprehensive guide, see [binaryonly_fuzzing.md](binaryonly_fuzzing.md). +For a comprehensive guide, see +[fuzzing_binary-only_targets.md](fuzzing_binary-only_targets.md). ### Fuzzing a GUI program @@ -48,7 +54,7 @@ to emulate the network. This is also much faster than the real network would be. See [utils/socket_fuzzing/](../utils/socket_fuzzing/). There is an outdated AFL++ branch that implements networking if you are -desperate though: [https://github.com/AFLplusplus/AFLplusplus/tree/networking](https://github.com/AFLplusplus/AFLplusplus/tree/networking) - +desperate though: [https://github.com/AFLplusplus/AFLplusplus/tree/networking](https://github.com/AFLplusplus/AFLplusplus/tree/networking) - however a better option is AFLnet ([https://github.com/aflnet/aflnet](https://github.com/aflnet/aflnet)) which allows you to define network state with different type of data packets. @@ -58,11 +64,11 @@ which allows you to define network state with different type of data packets. 1. Use [llvm_mode](../instrumentation/README.llvm.md): afl-clang-lto (llvm >= 11) or afl-clang-fast (llvm >= 9 recommended). 2. Use [persistent mode](../instrumentation/README.persistent_mode.md) (x2-x20 speed increase). -3. Use the [AFL++ snapshot module](https://github.com/AFLplusplus/AFL-Snapshot-LKM) (x2 speed increase). +3. Instrument just what you are interested in, see [instrumentation/README.instrument_list.md](../instrumentation/README.instrument_list.md). 4. If you do not use shmem persistent mode, use `AFL_TMPDIR` to put the input file directory on a tempfs location, see [env_variables.md](env_variables.md). 5. Improve Linux kernel performance: modify `/etc/default/grub`, set `GRUB_CMDLINE_LINUX_DEFAULT="ibpb=off ibrs=off kpti=off l1tf=off mds=off mitigations=off no_stf_barrier noibpb noibrs nopcid nopti nospec_store_bypass_disable nospectre_v1 nospectre_v2 pcid=off pti=off spec_store_bypass_disable=off spectre_v2=off stf_barrier=off"`; then `update-grub` and `reboot` (warning: makes the system less secure). 6. Running on an `ext2` filesystem with `noatime` mount option will be a bit faster than on any other journaling filesystem. -7. Use your cores! [fuzzing_expert.md:b) Using multiple cores](fuzzing_expert.md#b-using-multiple-cores). +7. Use your cores ([fuzzing_in_depth.md:3c) Using multiple cores](fuzzing_in_depth.md#c-using-multiple-cores))! ### Improving stability diff --git a/docs/beyond_crashes.md b/docs/beyond_crashes.md deleted file mode 100644 index 4836419c..00000000 --- a/docs/beyond_crashes.md +++ /dev/null @@ -1,23 +0,0 @@ -# Going beyond crashes - -Fuzzing is a wonderful and underutilized technique for discovering non-crashing -design and implementation errors, too. Quite a few interesting bugs have been -found by modifying the target programs to call abort() when say: - - - Two bignum libraries produce different outputs when given the same - fuzzer-generated input, - - - An image library produces different outputs when asked to decode the same - input image several times in a row, - - - A serialization / deserialization library fails to produce stable outputs - when iteratively serializing and deserializing fuzzer-supplied data, - - - A compression library produces an output inconsistent with the input file - when asked to compress and then decompress a particular blob. - -Implementing these or similar sanity checks usually takes very little time; -if you are the maintainer of a particular package, you can make this code -conditional with `#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION` (a flag also -shared with libfuzzer and honggfuzz) or `#ifdef __AFL_COMPILER` (this one is -just for AFL). \ No newline at end of file diff --git a/docs/binaryonly_fuzzing.md b/docs/binaryonly_fuzzing.md deleted file mode 100644 index 2c0872cf..00000000 --- a/docs/binaryonly_fuzzing.md +++ /dev/null @@ -1,225 +0,0 @@ -# Fuzzing binary-only programs with AFL++ - - AFL++, libfuzzer and others are great if you have the source code, and - it allows for very fast and coverage guided fuzzing. - - However, if there is only the binary program and no source code available, - then standard `afl-fuzz -n` (non-instrumented mode) is not effective. - - The following is a description of how these binaries can be fuzzed with AFL++. - - -## TL;DR: - - qemu_mode in persistent mode is the fastest - if the stability is - high enough. Otherwise try retrowrite, afl-dyninst and if these - fail too then try standard qemu_mode with AFL_ENTRYPOINT to where you need it. - - If your target is a library use utils/afl_frida/. - - If your target is non-linux then use unicorn_mode/. - - -## QEMU - - Qemu is the "native" solution to the program. - It is available in the ./qemu_mode/ directory and once compiled it can - be accessed by the afl-fuzz -Q command line option. - It is the easiest to use alternative and even works for cross-platform binaries. - - The speed decrease is at about 50%. - However various options exist to increase the speed: - - using AFL_ENTRYPOINT to move the forkserver entry to a later basic block in - the binary (+5-10% speed) - - using persistent mode [qemu_mode/README.persistent.md](../qemu_mode/README.persistent.md) - this will result in 150-300% overall speed increase - so 3-8x the original - qemu_mode speed! - - using AFL_CODE_START/AFL_CODE_END to only instrument specific parts - - Note that there is also honggfuzz: [https://github.com/google/honggfuzz](https://github.com/google/honggfuzz) - which now has a qemu_mode, but its performance is just 1.5% ... - - As it is included in AFL++ this needs no URL. - - If you like to code a customized fuzzer without much work, we highly - recommend to check out our sister project libafl which will support QEMU - too: - [https://github.com/AFLplusplus/LibAFL](https://github.com/AFLplusplus/LibAFL) - - -## AFL FRIDA - - In frida_mode you can fuzz binary-only targets easily like with QEMU, - with the advantage that frida_mode also works on MacOS (both intel and M1). - - If you want to fuzz a binary-only library then you can fuzz it with - frida-gum via utils/afl_frida/, you will have to write a harness to - call the target function in the library, use afl-frida.c as a template. - - Both come with AFL++ so this needs no URL. - - You can also perform remote fuzzing with frida, e.g. if you want to fuzz - on iPhone or Android devices, for this you can use - [https://github.com/ttdennis/fpicker/](https://github.com/ttdennis/fpicker/) - as an intermediate that uses AFL++ for fuzzing. - - If you like to code a customized fuzzer without much work, we highly - recommend to check out our sister project libafl which supports Frida too: - [https://github.com/AFLplusplus/LibAFL](https://github.com/AFLplusplus/LibAFL) - Working examples already exist :-) - - -## WINE+QEMU - - Wine mode can run Win32 PE binaries with the QEMU instrumentation. - It needs Wine, python3 and the pefile python package installed. - - As it is included in AFL++ this needs no URL. - - -## UNICORN - - Unicorn is a fork of QEMU. The instrumentation is, therefore, very similar. - In contrast to QEMU, Unicorn does not offer a full system or even userland - emulation. Runtime environment and/or loaders have to be written from scratch, - if needed. On top, block chaining has been removed. This means the speed boost - introduced in the patched QEMU Mode of AFL++ cannot simply be ported over to - Unicorn. For further information, check out [unicorn_mode/README.md](../unicorn_mode/README.md). - - As it is included in AFL++ this needs no URL. - - -## AFL UNTRACER - - If you want to fuzz a binary-only shared library then you can fuzz it with - utils/afl_untracer/, use afl-untracer.c as a template. - It is slower than AFL FRIDA (see above). - - -## ZAFL - ZAFL is a static rewriting platform supporting x86-64 C/C++, stripped/unstripped, - and PIE/non-PIE binaries. Beyond conventional instrumentation, ZAFL's API enables - transformation passes (e.g., laf-Intel, context sensitivity, InsTrim, etc.). - - Its baseline instrumentation speed typically averages 90-95% of afl-clang-fast's. - - [https://git.zephyr-software.com/opensrc/zafl](https://git.zephyr-software.com/opensrc/zafl) - - -## DYNINST - - Dyninst is a binary instrumentation framework similar to Pintool and - Dynamorio (see far below). However whereas Pintool and Dynamorio work at - runtime, dyninst instruments the target at load time, and then let it run - - or save the binary with the changes. - This is great for some things, e.g. fuzzing, and not so effective for others, - e.g. malware analysis. - - So what we can do with dyninst is taking every basic block, and put afl's - instrumention code in there - and then save the binary. - Afterwards we can just fuzz the newly saved target binary with afl-fuzz. - Sounds great? It is. The issue though - it is a non-trivial problem to - insert instructions, which change addresses in the process space, so that - everything is still working afterwards. Hence more often than not binaries - crash when they are run. - - The speed decrease is about 15-35%, depending on the optimization options - used with afl-dyninst. - - [https://github.com/vanhauser-thc/afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst) - - -## RETROWRITE - - If you have an x86/x86_64 binary that still has its symbols, is compiled - with position independant code (PIC/PIE) and does not use most of the C++ - features then the retrowrite solution might be for you. - It decompiles to ASM files which can then be instrumented with afl-gcc. - - It is at about 80-85% performance. - - [https://github.com/HexHive/retrowrite](https://github.com/HexHive/retrowrite) - - -## MCSEMA - - Theoretically you can also decompile to llvm IR with mcsema, and then - use llvm_mode to instrument the binary. - Good luck with that. - - [https://github.com/lifting-bits/mcsema](https://github.com/lifting-bits/mcsema) - - -## INTEL-PT - - If you have a newer Intel CPU, you can make use of Intels processor trace. - The big issue with Intel's PT is the small buffer size and the complex - encoding of the debug information collected through PT. - This makes the decoding very CPU intensive and hence slow. - As a result, the overall speed decrease is about 70-90% (depending on - the implementation and other factors). - - There are two AFL intel-pt implementations: - - 1. [https://github.com/junxzm1990/afl-pt](https://github.com/junxzm1990/afl-pt) - => this needs Ubuntu 14.04.05 without any updates and the 4.4 kernel. - - 2. [https://github.com/hunter-ht-2018/ptfuzzer](https://github.com/hunter-ht-2018/ptfuzzer) - => this needs a 4.14 or 4.15 kernel. the "nopti" kernel boot option must - be used. This one is faster than the other. - - Note that there is also honggfuzz: https://github.com/google/honggfuzz - But its IPT performance is just 6%! - - -## CORESIGHT - - Coresight is ARM's answer to Intel's PT. - With afl++ v3.15 there is a coresight tracer implementation available in - `coresight_mode/` which is faster than QEMU, however can not run in parallel. - Currently only one process can be traced, it is WIP. - - -## PIN & DYNAMORIO - - Pintool and Dynamorio are dynamic instrumentation engines, and they can be - used for getting basic block information at runtime. - Pintool is only available for Intel x32/x64 on Linux, Mac OS and Windows, - whereas Dynamorio is additionally available for ARM and AARCH64. - Dynamorio is also 10x faster than Pintool. - - The big issue with Dynamorio (and therefore Pintool too) is speed. - Dynamorio has a speed decrease of 98-99% - Pintool has a speed decrease of 99.5% - - Hence Dynamorio is the option to go for if everything else fails, and Pintool - only if Dynamorio fails too. - - Dynamorio solutions: - * [https://github.com/vanhauser-thc/afl-dynamorio](https://github.com/vanhauser-thc/afl-dynamorio) - * [https://github.com/mxmssh/drAFL](https://github.com/mxmssh/drAFL) - * [https://github.com/googleprojectzero/winafl/](https://github.com/googleprojectzero/winafl/) <= very good but windows only - - Pintool solutions: - * [https://github.com/vanhauser-thc/afl-pin](https://github.com/vanhauser-thc/afl-pin) - * [https://github.com/mothran/aflpin](https://github.com/mothran/aflpin) - * [https://github.com/spinpx/afl_pin_mode](https://github.com/spinpx/afl_pin_mode) <= only old Pintool version supported - - -## Non-AFL solutions - - There are many binary-only fuzzing frameworks. - Some are great for CTFs but don't work with large binaries, others are very - slow but have good path discovery, some are very hard to set-up ... - - * QSYM: [https://github.com/sslab-gatech/qsym](https://github.com/sslab-gatech/qsym) - * Manticore: [https://github.com/trailofbits/manticore](https://github.com/trailofbits/manticore) - * S2E: [https://github.com/S2E](https://github.com/S2E) - * Tinyinst: [https://github.com/googleprojectzero/TinyInst](https://github.com/googleprojectzero/TinyInst) (Mac/Windows only) - * Jackalope: [https://github.com/googleprojectzero/Jackalope](https://github.com/googleprojectzero/Jackalope) - * ... please send me any missing that are good - - -## Closing words - - That's it! News, corrections, updates? Send an email to vh@thc.org diff --git a/docs/branches.md b/docs/branches.md deleted file mode 100644 index ae147b08..00000000 --- a/docs/branches.md +++ /dev/null @@ -1,11 +0,0 @@ -# Branches - -The following branches exist: - -* [release](https://github.com/AFLplusplus/AFLplusplus/tree/release): the latest release -* [stable/trunk](https://github.com/AFLplusplus/AFLplusplus/): stable state of AFL++ - it is synced from dev from time to time when we are satisfied with its stability -* [dev](https://github.com/AFLplusplus/AFLplusplus/tree/dev): development state of AFL++ - bleeding edge and you might catch a checkout which does not compile or has a bug. *We only accept PRs in dev!!* -* (any other): experimental branches to work on specific features or testing new functionality or changes. - -For releases, please see the [Releases](https://github.com/AFLplusplus/AFLplusplus/releases) tab. -Also take a look at the list of [important changes in AFL++](important_changes.md). \ No newline at end of file diff --git a/docs/choosing_testcases.md b/docs/choosing_testcases.md deleted file mode 100644 index 25002929..00000000 --- a/docs/choosing_testcases.md +++ /dev/null @@ -1,19 +0,0 @@ -# Choosing initial test cases - -To operate correctly, the fuzzer requires one or more starting file that -contains a good example of the input data normally expected by the targeted -application. There are two basic rules: - - - Keep the files small. Under 1 kB is ideal, although not strictly necessary. - For a discussion of why size matters, see [perf_tips.md](perf_tips.md). - - - Use multiple test cases only if they are functionally different from - each other. There is no point in using fifty different vacation photos - to fuzz an image library. - -You can find many good examples of starting files in the testcases/ subdirectory -that comes with this tool. - -PS. If a large corpus of data is available for screening, you may want to use -the afl-cmin utility to identify a subset of functionally distinct files that -exercise different code paths in the target binary. \ No newline at end of file diff --git a/docs/ci_fuzzing.md b/docs/ci_fuzzing.md deleted file mode 100644 index 8d1a2f99..00000000 --- a/docs/ci_fuzzing.md +++ /dev/null @@ -1,29 +0,0 @@ -# CI Fuzzing - -Some notes on CI Fuzzing - this fuzzing is different to normal fuzzing campaigns as these are much shorter runnings. - -1. Always: - * LTO has a much longer compile time which is diametrical to short fuzzing - hence use afl-clang-fast instead. - * If you compile with CMPLOG then you can save fuzzing time and reuse that compiled target for both the -c option and the main fuzz target. - This will impact the speed by ~15% though. - * `AFL_FAST_CAL` - Enable fast calibration, this halfs the time the saturated corpus needs to be loaded. - * `AFL_CMPLOG_ONLY_NEW` - only perform cmplog on new found paths, not the initial corpus as this very likely has been done for them already. - * Keep the generated corpus, use afl-cmin and reuse it every time! - -2. Additionally randomize the AFL++ compilation options, e.g. - * 40% for `AFL_LLVM_CMPLOG` - * 10% for `AFL_LLVM_LAF_ALL` - -3. Also randomize the afl-fuzz runtime options, e.g. - * 65% for `AFL_DISABLE_TRIM` - * 50% use a dictionary generated by `AFL_LLVM_DICT2FILE` - * 40% use MOpt (`-L 0`) - * 40% for `AFL_EXPAND_HAVOC_NOW` - * 20% for old queue processing (`-Z`) - * for CMPLOG targets, 60% for `-l 2`, 40% for `-l 3` - -4. Do *not* run any `-M` modes, just running `-S` modes is better for CI fuzzing. -`-M` enables old queue handling etc. which is good for a fuzzing campaign but not good for short CI runs. - -How this can look like can e.g. be seen at AFL++'s setup in Google's [oss-fuzz](https://github.com/google/oss-fuzz/blob/master/infra/base-images/base-builder/compile_afl) -and [clusterfuzz](https://github.com/google/clusterfuzz/blob/master/src/clusterfuzz/_internal/bot/fuzzers/afl/launcher.py). diff --git a/docs/common_sense_risks.md b/docs/common_sense_risks.md deleted file mode 100644 index a8d68d7a..00000000 --- a/docs/common_sense_risks.md +++ /dev/null @@ -1,36 +0,0 @@ -# Common sense risks - -Please keep in mind that, similarly to many other computationally-intensive -tasks, fuzzing may put a strain on your hardware and on the OS. In particular: - - - Your CPU will run hot and will need adequate cooling. In most cases, if - cooling is insufficient or stops working properly, CPU speeds will be - automatically throttled. That said, especially when fuzzing on less - suitable hardware (laptops, smartphones, etc), it's not entirely impossible - for something to blow up. - - - Targeted programs may end up erratically grabbing gigabytes of memory or - filling up disk space with junk files. AFL++ tries to enforce basic memory - limits, but can't prevent each and every possible mishap. The bottom line - is that you shouldn't be fuzzing on systems where the prospect of data loss - is not an acceptable risk. - - - Fuzzing involves billions of reads and writes to the filesystem. On modern - systems, this will be usually heavily cached, resulting in fairly modest - "physical" I/O - but there are many factors that may alter this equation. - It is your responsibility to monitor for potential trouble; with very heavy - I/O, the lifespan of many HDDs and SSDs may be reduced. - - A good way to monitor disk I/O on Linux is the 'iostat' command: - -```shell - $ iostat -d 3 -x -k [...optional disk ID...] -``` - - Using the `AFL_TMPDIR` environment variable and a RAM-disk you can have the - heavy writing done in RAM to prevent the aforementioned wear and tear. For - example the following line will run a Docker container with all this preset: - - ```shell - # docker run -ti --mount type=tmpfs,destination=/ramdisk -e AFL_TMPDIR=/ramdisk aflplusplus/aflplusplus - ``` \ No newline at end of file diff --git a/docs/custom_mutators.md b/docs/custom_mutators.md index 8b5a4068..b1dfd309 100644 --- a/docs/custom_mutators.md +++ b/docs/custom_mutators.md @@ -127,9 +127,9 @@ def deinit(): # optional for Python - `describe` (optional): - When this function is called, it shall describe the current testcase, + When this function is called, it shall describe the current test case, generated by the last mutation. This will be called, for example, - to name the written testcase file after a crash occurred. + to name the written test case file after a crash occurred. Using it can help to reproduce crashing mutations. - `havoc_mutation` and `havoc_mutation_probability` (optional): @@ -224,7 +224,7 @@ Optionally, the following environment variables are supported: - `AFL_CUSTOM_MUTATOR_ONLY` - Disable all other mutation stages. This can prevent broken testcases + Disable all other mutation stages. This can prevent broken test cases (those that your Python module can't work with anymore) to fill up your queue. Best combined with a custom trimming routine (see below) because trimming can cause the same test breakage like havoc and splice. diff --git a/docs/env_variables.md b/docs/env_variables.md index 1a330158..c1c70ec5 100644 --- a/docs/env_variables.md +++ b/docs/env_variables.md @@ -143,7 +143,7 @@ Available options: - CLANG - outdated clang instrumentation - CLASSIC - classic AFL (map[cur_loc ^ prev_loc >> 1]++) (default) - You can also specify CTX and/or NGRAM, seperate the options with a comma "," + You can also specify CTX and/or NGRAM, separate the options with a comma "," then, e.g.: `AFL_LLVM_INSTRUMENT=CLASSIC,CTX,NGRAM-4` Note: It is actually not a good idea to use both CTX and NGRAM. :) @@ -303,8 +303,9 @@ checks or alter some of the more exotic semantics of the tool: exit soon after the first crash is found. - `AFL_CMPLOG_ONLY_NEW` will only perform the expensive cmplog feature for - newly found testcases and not for testcases that are loaded on startup (`-i - in`). This is an important feature to set when resuming a fuzzing session. + newly found test cases and not for test cases that are loaded on startup + (`-i in`). This is an important feature to set when resuming a fuzzing + session. - Setting `AFL_CRASH_EXITCODE` sets the exit code AFL treats as crash. For example, if `AFL_CRASH_EXITCODE='-1'` is set, each input resulting in a `-1` @@ -444,8 +445,8 @@ checks or alter some of the more exotic semantics of the tool: - If you are using persistent mode (you should, see [instrumentation/README.persistent_mode.md](../instrumentation/README.persistent_mode.md)), - some targets keep inherent state due which a detected crash testcase does - not crash the target again when the testcase is given. To be able to still + some targets keep inherent state due which a detected crash test case does + not crash the target again when the test case is given. To be able to still re-trigger these crashes, you can use the `AFL_PERSISTENT_RECORD` variable with a value of how many previous fuzz cases to keep prio a crash. If set to e.g. 10, then the 9 previous inputs are written to out/default/crashes as @@ -523,23 +524,23 @@ checks or alter some of the more exotic semantics of the tool: The QEMU wrapper used to instrument binary-only code supports several settings: - Setting `AFL_COMPCOV_LEVEL` enables the CompareCoverage tracing of all cmp - and sub in x86 and x86_64 and memory comparions functions (e.g. strcmp, + and sub in x86 and x86_64 and memory comparison functions (e.g., strcmp, memcmp, ...) when libcompcov is preloaded using `AFL_PRELOAD`. More info at [qemu_mode/libcompcov/README.md](../qemu_mode/libcompcov/README.md). There are two levels at the moment, `AFL_COMPCOV_LEVEL=1` that instruments only comparisons with immediate values / read-only memory and - `AFL_COMPCOV_LEVEL=2` that instruments all the comparions. Level 2 is more + `AFL_COMPCOV_LEVEL=2` that instruments all the comparisons. Level 2 is more accurate but may need a larger shared memory. - - `AFL_DEBUG` will print the found entrypoint for the binary to stderr. Use - this if you are unsure if the entrypoint might be wrong - but use it + - `AFL_DEBUG` will print the found entry point for the binary to stderr. Use + this if you are unsure if the entry point might be wrong - but use it directly, e.g. `afl-qemu-trace ./program`. - - `AFL_ENTRYPOINT` allows you to specify a specific entrypoint into the binary - (this can be very good for the performance!). The entrypoint is specified as - hex address, e.g. `0x4004110`. Note that the address must be the address of - a basic block. + - `AFL_ENTRYPOINT` allows you to specify a specific entry point into the + binary (this can be very good for the performance!). The entry point is + specified as hex address, e.g. `0x4004110`. Note that the address must be + the address of a basic block. - Setting `AFL_INST_LIBS` causes the translator to also instrument the code inside any dynamically linked libraries (notably including glibc). diff --git a/docs/features.md b/docs/features.md index f44e32ff..35a869a9 100644 --- a/docs/features.md +++ b/docs/features.md @@ -1,49 +1,61 @@ # Important features of AFL++ - AFL++ supports llvm from 3.8 up to version 12, very fast binary fuzzing with QEMU 5.1 - with laf-intel and redqueen, frida mode, unicorn mode, gcc plugin, full *BSD, - Mac OS, Solaris and Android support and much, much, much more. +AFL++ supports llvm from 3.8 up to version 12, very fast binary fuzzing with +QEMU 5.1 with laf-intel and redqueen, frida mode, unicorn mode, gcc plugin, full +*BSD, Mac OS, Solaris and Android support and much, much, much more. - | Feature/Instrumentation | afl-gcc | llvm | gcc_plugin | frida_mode(9) | qemu_mode(10) |unicorn_mode(10) |coresight_mode(11)| - | -------------------------|:-------:|:---------:|:----------:|:----------------:|:----------------:|:----------------:|:----------------:| - | Threadsafe counters | | x(3) | | | | | | - | NeverZero | x86[_64]| x(1) | x | x | x | x | | - | Persistent Mode | | x | x | x86[_64]/arm64 | x86[_64]/arm[64] | x | | - | LAF-Intel / CompCov | | x | | | x86[_64]/arm[64] | x86[_64]/arm[64] | | - | CmpLog | | x | | x86[_64]/arm64 | x86[_64]/arm[64] | | | - | Selective Instrumentation| | x | x | x | x | | | - | Non-Colliding Coverage | | x(4) | | | (x)(5) | | | - | Ngram prev_loc Coverage | | x(6) | | | | | | - | Context Coverage | | x(6) | | | | | | - | Auto Dictionary | | x(7) | | | | | | - | Snapshot LKM Support | | (x)(8) | (x)(8) | | (x)(5) | | | - | Shared Memory Testcases | | x | x | x86[_64]/arm64 | x | x | | +| Feature/Instrumentation | afl-gcc | llvm | gcc_plugin | frida_mode(9) | qemu_mode(10) |unicorn_mode(10) |coresight_mode(11)| +| -------------------------|:-------:|:---------:|:----------:|:----------------:|:----------------:|:----------------:|:----------------:| +| Threadsafe counters | | x(3) | | | | | | +| NeverZero | x86[_64]| x(1) | x | x | x | x | | +| Persistent Mode | | x | x | x86[_64]/arm64 | x86[_64]/arm[64] | x | | +| LAF-Intel / CompCov | | x | | | x86[_64]/arm[64] | x86[_64]/arm[64] | | +| CmpLog | | x | | x86[_64]/arm64 | x86[_64]/arm[64] | | | +| Selective Instrumentation| | x | x | x | x | | | +| Non-Colliding Coverage | | x(4) | | | (x)(5) | | | +| Ngram prev_loc Coverage | | x(6) | | | | | | +| Context Coverage | | x(6) | | | | | | +| Auto Dictionary | | x(7) | | | | | | +| Snapshot LKM Support | | (x)(8) | (x)(8) | | (x)(5) | | | +| Shared Memory Test cases | | x | x | x86[_64]/arm64 | x | x | | - 1. default for LLVM >= 9.0, env var for older version due an efficiency bug in previous llvm versions - 2. GCC creates non-performant code, hence it is disabled in gcc_plugin - 3. with `AFL_LLVM_THREADSAFE_INST`, disables NeverZero - 4. with pcguard mode and LTO mode for LLVM 11 and newer - 5. upcoming, development in the branch - 6. not compatible with LTO instrumentation and needs at least LLVM v4.1 - 7. automatic in LTO mode with LLVM 11 and newer, an extra pass for all LLVM versions that write to a file to use with afl-fuzz' `-x` - 8. the snapshot LKM is currently unmaintained due to too many kernel changes coming too fast :-( - 9. frida mode is supported on Linux and MacOS for Intel and ARM - 10. QEMU/Unicorn is only supported on Linux - 11. Coresight mode is only available on AARCH64 Linux with a CPU with Coresight extension +1. default for LLVM >= 9.0, env var for older version due an efficiency bug in + previous llvm versions +2. GCC creates non-performant code, hence it is disabled in gcc_plugin +3. with `AFL_LLVM_THREADSAFE_INST`, disables NeverZero +4. with pcguard mode and LTO mode for LLVM 11 and newer +5. upcoming, development in the branch +6. not compatible with LTO instrumentation and needs at least LLVM v4.1 +7. automatic in LTO mode with LLVM 11 and newer, an extra pass for all LLVM + versions that write to a file to use with afl-fuzz' `-x` +8. the snapshot LKM is currently unmaintained due to too many kernel changes + coming too fast :-( +9. frida mode is supported on Linux and MacOS for Intel and ARM +10. QEMU/Unicorn is only supported on Linux +11. Coresight mode is only available on AARCH64 Linux with a CPU with Coresight + extension - Among others, the following features and patches have been integrated: +Among others, the following features and patches have been integrated: - * NeverZero patch for afl-gcc, instrumentation, qemu_mode and unicorn_mode which prevents a wrapping map value to zero, increases coverage - * Persistent mode, deferred forkserver and in-memory fuzzing for qemu_mode - * Unicorn mode which allows fuzzing of binaries from completely different platforms (integration provided by domenukk) - * The new CmpLog instrumentation for LLVM and QEMU inspired by [Redqueen](https://www.syssec.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2018/12/17/NDSS19-Redqueen.pdf) - * Win32 PE binary-only fuzzing with QEMU and Wine - * AFLfast's power schedules by Marcel Böhme: [https://github.com/mboehme/aflfast](https://github.com/mboehme/aflfast) - * The MOpt mutator: [https://github.com/puppet-meteor/MOpt-AFL](https://github.com/puppet-meteor/MOpt-AFL) - * LLVM mode Ngram coverage by Adrian Herrera [https://github.com/adrianherrera/afl-ngram-pass](https://github.com/adrianherrera/afl-ngram-pass) - * LAF-Intel/CompCov support for instrumentation, qemu_mode and unicorn_mode (with enhanced capabilities) - * Radamsa and honggfuzz mutators (as custom mutators). - * QBDI mode to fuzz android native libraries via Quarkslab's [QBDI](https://github.com/QBDI/QBDI) framework - * Frida and ptrace mode to fuzz binary-only libraries, etc. +* NeverZero patch for afl-gcc, instrumentation, qemu_mode and unicorn_mode which + prevents a wrapping map value to zero, increases coverage +* Persistent mode, deferred forkserver and in-memory fuzzing for qemu_mode +* Unicorn mode which allows fuzzing of binaries from completely different + platforms (integration provided by domenukk) +* The new CmpLog instrumentation for LLVM and QEMU inspired by + [Redqueen](https://www.syssec.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2018/12/17/NDSS19-Redqueen.pdf) +* Win32 PE binary-only fuzzing with QEMU and Wine +* AFLfast's power schedules by Marcel Böhme: + [https://github.com/mboehme/aflfast](https://github.com/mboehme/aflfast) +* The MOpt mutator: + [https://github.com/puppet-meteor/MOpt-AFL](https://github.com/puppet-meteor/MOpt-AFL) +* LLVM mode Ngram coverage by Adrian Herrera + [https://github.com/adrianherrera/afl-ngram-pass](https://github.com/adrianherrera/afl-ngram-pass) +* LAF-Intel/CompCov support for instrumentation, qemu_mode and unicorn_mode + (with enhanced capabilities) +* Radamsa and honggfuzz mutators (as custom mutators). +* QBDI mode to fuzz android native libraries via Quarkslab's + [QBDI](https://github.com/QBDI/QBDI) framework +* Frida and ptrace mode to fuzz binary-only libraries, etc. - So all in all this is the best-of AFL that is out there :-) \ No newline at end of file +So all in all this is the best-of AFL that is out there :-) \ No newline at end of file diff --git a/docs/fuzzing_binary-only_targets.md b/docs/fuzzing_binary-only_targets.md index ea262f6e..290c9bec 100644 --- a/docs/fuzzing_binary-only_targets.md +++ b/docs/fuzzing_binary-only_targets.md @@ -1,83 +1,293 @@ # Fuzzing binary-only targets -When source code is *NOT* available, AFL++ offers various support for fast, -on-the-fly instrumentation of black-box binaries. +AFL++, libfuzzer, and other fuzzers are great if you have the source code of the +target. This allows for very fast and coverage guided fuzzing. -If you do not have to use Unicorn the following setup is recommended to use -qemu_mode: - * run 1 afl-fuzz -Q instance with CMPLOG (`-c 0` + `AFL_COMPCOV_LEVEL=2`) - * run 1 afl-fuzz -Q instance with QASAN (`AFL_USE_QASAN=1`) - * run 1 afl-fuzz -Q instance with LAF (`AFL_PRELOAD=libcmpcov.so` + `AFL_COMPCOV_LEVEL=2`) -Alternatively you can use frida_mode, just switch `-Q` with `-O` and remove the -LAF instance. +However, if there is only the binary program and no source code available, then +standard `afl-fuzz -n` (non-instrumented mode) is not effective. -Then run as many instances as you have cores left with either -Q mode or - better - -use a binary rewriter like afl-dyninst, retrowrite, zafl, etc. +For fast, on-the-fly instrumentation of black-box binaries, AFL++ still offers +various support. The following is a description of how these binaries can be +fuzzed with AFL++. -For Qemu and Frida mode, check out the persistent mode, it gives a huge speed -improvement if it is possible to use. +## TL;DR: -### QEMU +Qemu_mode in persistent mode is the fastest - if the stability is high enough. +Otherwise, try RetroWrite, Dyninst, and if these fail, too, then try standard +qemu_mode with AFL_ENTRYPOINT to where you need it. -For linux programs and its libraries this is accomplished with a version of -QEMU running in the lesser-known "user space emulation" mode. -QEMU is a project separate from AFL, but you can conveniently build the -feature by doing: +If your target is a library, then use frida_mode. + +If your target is non-linux, then use unicorn_mode. + +## Fuzzing binary-only targets with AFL++ +### Qemu_mode + +Qemu_mode is the "native" solution to the program. It is available in the +./qemu_mode/ directory and, once compiled, it can be accessed by the afl-fuzz -Q +command line option. It is the easiest to use alternative and even works for +cross-platform binaries. + +For linux programs and its libraries, this is accomplished with a version of +QEMU running in the lesser-known "user space emulation" mode. QEMU is a project +separate from AFL++, but you can conveniently build the feature by doing: ```shell cd qemu_mode ./build_qemu_support.sh ``` -For additional instructions and caveats, see [qemu_mode/README.md](../qemu_mode/README.md). -If possible you should use the persistent mode, see [qemu_mode/README.persistent.md](../qemu_mode/README.persistent.md). -The mode is approximately 2-5x slower than compile-time instrumentation, and is -less conducive to parallelization. +The following setup to use qemu_mode is recommended: +* run 1 afl-fuzz -Q instance with CMPLOG (`-c 0` + `AFL_COMPCOV_LEVEL=2`) +* run 1 afl-fuzz -Q instance with QASAN (`AFL_USE_QASAN=1`) +* run 1 afl-fuzz -Q instance with LAF (`AFL_PRELOAD=libcmpcov.so` + + `AFL_COMPCOV_LEVEL=2`), alternatively you can use frida_mode, just switch `-Q` + with `-O` and remove the LAF instance + +Then run as many instances as you have cores left with either -Q mode or - even +better - use a binary rewriter like Dyninst, RetroWrite, ZAFL, etc. + +If [afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst) works for your +binary, then you can use afl-fuzz normally and it will have twice the speed +compared to qemu_mode (but slower than qemu persistent mode). Note that several +other binary rewriters exist, all with their advantages and caveats. + +The speed decrease of qemu_mode is at about 50%. However, various options exist +to increase the speed: +- using AFL_ENTRYPOINT to move the forkserver entry to a later basic block in + the binary (+5-10% speed) +- using persistent mode + [qemu_mode/README.persistent.md](../qemu_mode/README.persistent.md) this will + result in a 150-300% overall speed increase - so 3-8x the original qemu_mode + speed! +- using AFL_CODE_START/AFL_CODE_END to only instrument specific parts + +For additional instructions and caveats, see +[qemu_mode/README.md](../qemu_mode/README.md). If possible, you should use the +persistent mode, see +[qemu_mode/README.persistent.md](../qemu_mode/README.persistent.md). The mode is +approximately 2-5x slower than compile-time instrumentation, and is less +conducive to parallelization. -If [afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst) works for -your binary, then you can use afl-fuzz normally and it will have twice -the speed compared to qemu_mode (but slower than qemu persistent mode). -Note that several other binary rewriters exist, all with their advantages and -caveats. +Note that there is also honggfuzz: +[https://github.com/google/honggfuzz](https://github.com/google/honggfuzz) which +now has a qemu_mode, but its performance is just 1.5% ... -### Frida +If you like to code a customized fuzzer without much work, we highly recommend +to check out our sister project libafl which supports QEMU, too: +[https://github.com/AFLplusplus/LibAFL](https://github.com/AFLplusplus/LibAFL) -Frida mode is sometimes faster and sometimes slower than Qemu mode. -It is also newer, lacks COMPCOV, but supports MacOS. +### WINE+QEMU + +Wine mode can run Win32 PE binaries with the QEMU instrumentation. It needs +Wine, python3, and the pefile python package installed. + +It is included in AFL++. + +For more information, see [qemu_mode/README.wine.md](../qemu_mode/README.wine.md). + +### Frida_mode + +In frida_mode, you can fuzz binary-only targets as easily as with QEMU. +Frida_mode is sometimes faster and sometimes slower than Qemu_mode. It is also +newer, lacks COMPCOV, and has the advantage that it works on MacOS (both intel +and M1). + +To build frida_mode: ```shell cd frida_mode make ``` -For additional instructions and caveats, see [frida_mode/README.md](../frida_mode/README.md). -If possible you should use the persistent mode, see [qemu_frida/README.md](../qemu_frida/README.md). -The mode is approximately 2-5x slower than compile-time instrumentation, and is -less conducive to parallelization. +For additional instructions and caveats, see +[frida_mode/README.md](../frida_mode/README.md). + +If possible, you should use the persistent mode, see +[qemu_frida/README.md](../qemu_frida/README.md). The mode is approximately 2-5x +slower than compile-time instrumentation, and is less conducive to +parallelization. But for binary-only fuzzing, it gives a huge speed improvement +if it is possible to use. + +If you want to fuzz a binary-only library, then you can fuzz it with frida-gum +via frida_mode/. You will have to write a harness to call the target function in +the library, use afl-frida.c as a template. + +You can also perform remote fuzzing with frida, e.g. if you want to fuzz on +iPhone or Android devices, for this you can use +[https://github.com/ttdennis/fpicker/](https://github.com/ttdennis/fpicker/) as +an intermediate that uses AFL++ for fuzzing. + +If you like to code a customized fuzzer without much work, we highly recommend +to check out our sister project libafl which supports Frida, too: +[https://github.com/AFLplusplus/LibAFL](https://github.com/AFLplusplus/LibAFL). +Working examples already exist :-) ### Unicorn -For non-Linux binaries you can use AFL++'s unicorn mode which can emulate +Unicorn is a fork of QEMU. The instrumentation is, therefore, very similar. In +contrast to QEMU, Unicorn does not offer a full system or even userland +emulation. Runtime environment and/or loaders have to be written from scratch, +if needed. On top, block chaining has been removed. This means the speed boost +introduced in the patched QEMU Mode of AFL++ cannot simply be ported over to +Unicorn. + +For non-Linux binaries, you can use AFL++'s unicorn_mode which can emulate anything you want - for the price of speed and user written scripts. -See [unicorn_mode/README.md](../unicorn_mode/README.md). -It can be easily built by: +To build unicorn_mode: + ```shell cd unicorn_mode ./build_unicorn_support.sh ``` +For further information, check out +[unicorn_mode/README.md](../unicorn_mode/README.md). + ### Shared libraries -If the goal is to fuzz a dynamic library then there are two options available. -For both you need to write a small harness that loads and calls the library. -Then you fuzz this with either frida_mode or qemu_mode, and either use +If the goal is to fuzz a dynamic library, then there are two options available. +For both, you need to write a small harness that loads and calls the library. +Then you fuzz this with either frida_mode or qemu_mode and either use `AFL_INST_LIBS=1` or `AFL_QEMU/FRIDA_INST_RANGES`. -Another, less precise and slower option is using ptrace with debugger interrupt -instrumentation: [utils/afl_untracer/README.md](../utils/afl_untracer/README.md). +Another, less precise and slower option is to fuzz it with utils/afl_untracer/ +and use afl-untracer.c as a template. It is slower than frida_mode. + +For more information, see +[utils/afl_untracer/README.md](../utils/afl_untracer/README.md). + +### Coresight + +Coresight is ARM's answer to Intel's PT. With AFL++ v3.15, there is a coresight +tracer implementation available in `coresight_mode/` which is faster than QEMU, +however, cannot run in parallel. Currently, only one process can be traced, it +is WIP. + +Fore more information, see +[coresight_mode/README.md](../coresight_mode/README.md). + +## Binary rewriters + +An alternative solution are binary rewriters. They are faster then the solutions native to AFL++ but don't always work. + +### ZAFL +ZAFL is a static rewriting platform supporting x86-64 C/C++, +stripped/unstripped, and PIE/non-PIE binaries. Beyond conventional +instrumentation, ZAFL's API enables transformation passes (e.g., laf-Intel, +context sensitivity, InsTrim, etc.). + +Its baseline instrumentation speed typically averages 90-95% of +afl-clang-fast's. + +[https://git.zephyr-software.com/opensrc/zafl](https://git.zephyr-software.com/opensrc/zafl) + +### RetroWrite + +If you have an x86/x86_64 binary that still has its symbols, is compiled with +position independent code (PIC/PIE), and does not use most of the C++ features, +then the RetroWrite solution might be for you. It decompiles to ASM files which +can then be instrumented with afl-gcc. + +It is at about 80-85% performance. + +[https://github.com/HexHive/retrowrite](https://github.com/HexHive/retrowrite) + +### Dyninst + +Dyninst is a binary instrumentation framework similar to Pintool and DynamoRIO. +However, whereas Pintool and DynamoRIO work at runtime, Dyninst instruments the +target at load time and then let it run - or save the binary with the changes. +This is great for some things, e.g. fuzzing, and not so effective for others, +e.g. malware analysis. + +So, what we can do with Dyninst is taking every basic block and put AFL++'s +instrumentation code in there - and then save the binary. Afterwards, we can +just fuzz the newly saved target binary with afl-fuzz. Sounds great? It is. The +issue though - it is a non-trivial problem to insert instructions, which change +addresses in the process space, so that everything is still working afterwards. +Hence, more often than not binaries crash when they are run. + +The speed decrease is about 15-35%, depending on the optimization options used +with afl-dyninst. + +[https://github.com/vanhauser-thc/afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst) + +### Mcsema + +Theoretically, you can also decompile to llvm IR with mcsema, and then use +llvm_mode to instrument the binary. Good luck with that. + +[https://github.com/lifting-bits/mcsema](https://github.com/lifting-bits/mcsema) + +## Binary tracers + +### Pintool & DynamoRIO + +Pintool and DynamoRIO are dynamic instrumentation engines. They can be used for +getting basic block information at runtime. Pintool is only available for Intel +x32/x64 on Linux, Mac OS, and Windows, whereas DynamoRIO is additionally +available for ARM and AARCH64. DynamoRIO is also 10x faster than Pintool. + +The big issue with DynamoRIO (and therefore Pintool, too) is speed. DynamoRIO +has a speed decrease of 98-99%, Pintool has a speed decrease of 99.5%. + +Hence, DynamoRIO is the option to go for if everything else fails and Pintool +only if DynamoRIO fails, too. + +DynamoRIO solutions: +* [https://github.com/vanhauser-thc/afl-dynamorio](https://github.com/vanhauser-thc/afl-dynamorio) +* [https://github.com/mxmssh/drAFL](https://github.com/mxmssh/drAFL) +* [https://github.com/googleprojectzero/winafl/](https://github.com/googleprojectzero/winafl/) + <= very good but windows only + +Pintool solutions: +* [https://github.com/vanhauser-thc/afl-pin](https://github.com/vanhauser-thc/afl-pin) +* [https://github.com/mothran/aflpin](https://github.com/mothran/aflpin) +* [https://github.com/spinpx/afl_pin_mode](https://github.com/spinpx/afl_pin_mode) + <= only old Pintool version supported + +### Intel PT + +If you have a newer Intel CPU, you can make use of Intel's processor trace. The +big issue with Intel's PT is the small buffer size and the complex encoding of +the debug information collected through PT. This makes the decoding very CPU +intensive and hence slow. As a result, the overall speed decrease is about +70-90% (depending on the implementation and other factors). + +There are two AFL intel-pt implementations: + +1. [https://github.com/junxzm1990/afl-pt](https://github.com/junxzm1990/afl-pt) + => This needs Ubuntu 14.04.05 without any updates and the 4.4 kernel. + +2. [https://github.com/hunter-ht-2018/ptfuzzer](https://github.com/hunter-ht-2018/ptfuzzer) + => This needs a 4.14 or 4.15 kernel. The "nopti" kernel boot option must be + used. This one is faster than the other. + +Note that there is also honggfuzz: +[https://github.com/google/honggfuzz](https://github.com/google/honggfuzz). But +its IPT performance is just 6%! + +## Non-AFL++ solutions + +There are many binary-only fuzzing frameworks. Some are great for CTFs but don't +work with large binaries, others are very slow but have good path discovery, +some are very hard to set-up... + + +* Jackalope: + [https://github.com/googleprojectzero/Jackalope](https://github.com/googleprojectzero/Jackalope) +* Manticore: + [https://github.com/trailofbits/manticore](https://github.com/trailofbits/manticore) +* QSYM: + [https://github.com/sslab-gatech/qsym](https://github.com/sslab-gatech/qsym) +* S2E: [https://github.com/S2E](https://github.com/S2E) +* TinyInst: + [https://github.com/googleprojectzero/TinyInst](https://github.com/googleprojectzero/TinyInst) + (Mac/Windows only) +* ... please send me any missing that are good -### More +## Closing words -A more comprehensive description of these and other options can be found in -[binaryonly_fuzzing.md](binaryonly_fuzzing.md). \ No newline at end of file +That's it! News, corrections, updates? Send an email to vh@thc.org. \ No newline at end of file diff --git a/docs/fuzzing_expert.md b/docs/fuzzing_expert.md deleted file mode 100644 index d0d28582..00000000 --- a/docs/fuzzing_expert.md +++ /dev/null @@ -1,626 +0,0 @@ -# Fuzzing with AFL++ - -The following describes how to fuzz with a target if source code is available. -If you have a binary-only target please skip to [#Instrumenting binary-only apps](#Instrumenting binary-only apps) - -Fuzzing source code is a three-step process. - -1. Compile the target with a special compiler that prepares the target to be - fuzzed efficiently. This step is called "instrumenting a target". -2. Prepare the fuzzing by selecting and optimizing the input corpus for the - target. -3. Perform the fuzzing of the target by randomly mutating input and assessing - if a generated input was processed in a new path in the target binary. - -### 1. Instrumenting that target - -#### a) Selecting the best AFL++ compiler for instrumenting the target - -AFL++ comes with a central compiler `afl-cc` that incorporates various different -kinds of compiler targets and and instrumentation options. -The following evaluation flow will help you to select the best possible. - -It is highly recommended to have the newest llvm version possible installed, -anything below 9 is not recommended. - -``` -+--------------------------------+ -| clang/clang++ 11+ is available | --> use LTO mode (afl-clang-lto/afl-clang-lto++) -+--------------------------------+ see [instrumentation/README.lto.md](instrumentation/README.lto.md) - | - | if not, or if the target fails with LTO afl-clang-lto/++ - | - v -+---------------------------------+ -| clang/clang++ 3.8+ is available | --> use LLVM mode (afl-clang-fast/afl-clang-fast++) -+---------------------------------+ see [instrumentation/README.llvm.md](instrumentation/README.llvm.md) - | - | if not, or if the target fails with LLVM afl-clang-fast/++ - | - v - +--------------------------------+ - | gcc 5+ is available | -> use GCC_PLUGIN mode (afl-gcc-fast/afl-g++-fast) - +--------------------------------+ see [instrumentation/README.gcc_plugin.md](instrumentation/README.gcc_plugin.md) and - [instrumentation/README.instrument_list.md](instrumentation/README.instrument_list.md) - | - | if not, or if you do not have a gcc with plugin support - | - v - use GCC mode (afl-gcc/afl-g++) (or afl-clang/afl-clang++ for clang) -``` - -Clickable README links for the chosen compiler: - - * [LTO mode - afl-clang-lto](../instrumentation/README.lto.md) - * [LLVM mode - afl-clang-fast](../instrumentation/README.llvm.md) - * [GCC_PLUGIN mode - afl-gcc-fast](../instrumentation/README.gcc_plugin.md) - * GCC/CLANG modes (afl-gcc/afl-clang) have no README as they have no own features - -You can select the mode for the afl-cc compiler by: - 1. use a symlink to afl-cc: afl-gcc, afl-g++, afl-clang, afl-clang++, - afl-clang-fast, afl-clang-fast++, afl-clang-lto, afl-clang-lto++, - afl-gcc-fast, afl-g++-fast (recommended!) - 2. using the environment variable AFL_CC_COMPILER with MODE - 3. passing --afl-MODE command line options to the compiler via CFLAGS/CXXFLAGS/CPPFLAGS - -MODE can be one of: LTO (afl-clang-lto*), LLVM (afl-clang-fast*), GCC_PLUGIN -(afl-g*-fast) or GCC (afl-gcc/afl-g++) or CLANG(afl-clang/afl-clang++). - -Because no AFL specific command-line options are accepted (beside the ---afl-MODE command), the compile-time tools make fairly broad use of environment -variables, which can be listed with `afl-cc -hh` or by reading [env_variables.md](env_variables.md). - -#### b) Selecting instrumentation options - -The following options are available when you instrument with LTO mode (afl-clang-fast/afl-clang-lto): - - * Splitting integer, string, float and switch comparisons so AFL++ can easier - solve these. This is an important option if you do not have a very good - and large input corpus. This technique is called laf-intel or COMPCOV. - To use this set the following environment variable before compiling the - target: `export AFL_LLVM_LAF_ALL=1` - You can read more about this in [instrumentation/README.laf-intel.md](../instrumentation/README.laf-intel.md) - * A different technique (and usually a better one than laf-intel) is to - instrument the target so that any compare values in the target are sent to - AFL++ which then tries to put these values into the fuzzing data at different - locations. This technique is very fast and good - if the target does not - transform input data before comparison. Therefore this technique is called - `input to state` or `redqueen`. - If you want to use this technique, then you have to compile the target - twice, once specifically with/for this mode by setting `AFL_LLVM_CMPLOG=1`, - and pass this binary to afl-fuzz via the `-c` parameter. - Note that you can compile also just a cmplog binary and use that for both - however there will be a performance penality. - You can read more about this in [instrumentation/README.cmplog.md](../instrumentation/README.cmplog.md) - -If you use LTO, LLVM or GCC_PLUGIN mode (afl-clang-fast/afl-clang-lto/afl-gcc-fast) -you have the option to selectively only instrument parts of the target that you -are interested in: - - * To instrument only those parts of the target that you are interested in - create a file with all the filenames of the source code that should be - instrumented. - For afl-clang-lto and afl-gcc-fast - or afl-clang-fast if a mode other than - DEFAULT/PCGUARD is used or you have llvm > 10.0.0 - just put one - filename or function per line (no directory information necessary for - filenames9, and either set `export AFL_LLVM_ALLOWLIST=allowlist.txt` **or** - `export AFL_LLVM_DENYLIST=denylist.txt` - depending on if you want per - default to instrument unless noted (DENYLIST) or not perform instrumentation - unless requested (ALLOWLIST). - **NOTE:** During optimization functions might be inlined and then would not match! - See [instrumentation/README.instrument_list.md](../instrumentation/README.instrument_list.md) - -There are many more options and modes available however these are most of the -time less effective. See: - * [instrumentation/README.llvm.md#6) AFL++ Context Sensitive Branch Coverage](../instrumentation/README.llvm.md#6-afl-context-sensitive-branch-coverage). - * [instrumentation/README.llvm.md#7) AFL++ N-Gram Branch Coverage](../instrumentation/README.llvm.md#7-afl-n-gram-branch-coverage) - -#### c) Sanitizers - -It is possible to use sanitizers when instrumenting targets for fuzzing, -which allows you to find bugs that would not necessarily result in a crash. - -Note that sanitizers have a huge impact on CPU (= less executions per second) -and RAM usage. Also you should only run one afl-fuzz instance per sanitizer type. -This is enough because a use-after-free bug will be picked up, e.g. by -ASAN (address sanitizer) anyway when syncing to other fuzzing instances, -so not all fuzzing instances need to be instrumented with ASAN. - -The following sanitizers have built-in support in AFL++: - * ASAN = Address SANitizer, finds memory corruption vulnerabilities like - use-after-free, NULL pointer dereference, buffer overruns, etc. - Enabled with `export AFL_USE_ASAN=1` before compiling. - * MSAN = Memory SANitizer, finds read access to uninitialized memory, eg. - a local variable that is defined and read before it is even set. - Enabled with `export AFL_USE_MSAN=1` before compiling. - * UBSAN = Undefined Behaviour SANitizer, finds instances where - by the - C and C++ standards - undefined behaviour happens, e.g. adding two - signed integers together where the result is larger than a signed integer - can hold. - Enabled with `export AFL_USE_UBSAN=1` before compiling. - * CFISAN = Control Flow Integrity SANitizer, finds instances where the - control flow is found to be illegal. Originally this was rather to - prevent return oriented programming exploit chains from functioning, - in fuzzing this is mostly reduced to detecting type confusion - vulnerabilities - which is however one of the most important and dangerous - C++ memory corruption classes! - Enabled with `export AFL_USE_CFISAN=1` before compiling. - * TSAN = Thread SANitizer, finds thread race conditions. - Enabled with `export AFL_USE_TSAN=1` before compiling. - * LSAN = Leak SANitizer, finds memory leaks in a program. This is not really - a security issue, but for developers this can be very valuable. - Note that unlike the other sanitizers above this needs - `__AFL_LEAK_CHECK();` added to all areas of the target source code where you - find a leak check necessary! - Enabled with `export AFL_USE_LSAN=1` before compiling. - -It is possible to further modify the behaviour of the sanitizers at run-time -by setting `ASAN_OPTIONS=...`, `LSAN_OPTIONS` etc. - the available parameters -can be looked up in the sanitizer documentation of llvm/clang. -afl-fuzz however requires some specific parameters important for fuzzing to be -set. If you want to set your own, it might bail and report what it is missing. - -Note that some sanitizers cannot be used together, e.g. ASAN and MSAN, and -others often cannot work together because of target weirdness, e.g. ASAN and -CFISAN. You might need to experiment which sanitizers you can combine in a -target (which means more instances can be run without a sanitized target, -which is more effective). - -#### d) Modify the target - -If the target has features that make fuzzing more difficult, e.g. -checksums, HMAC, etc. then modify the source code so that checks for these -values are removed. -This can even be done safely for source code used in operational products -by eliminating these checks within these AFL specific blocks: - -``` -#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION - // say that the checksum or HMAC was fine - or whatever is required - // to eliminate the need for the fuzzer to guess the right checksum - return 0; -#endif -``` - -All AFL++ compilers will set this preprocessor definition automatically. - -#### e) Instrument the target - -In this step the target source code is compiled so that it can be fuzzed. - -Basically you have to tell the target build system that the selected AFL++ -compiler is used. Also - if possible - you should always configure the -build system such that the target is compiled statically and not dynamically. -How to do this is described below. - -The #1 rule when instrumenting a target is: avoid instrumenting shared -libraries at all cost. You would need to set LD_LIBRARY_PATH to point to -these, you could accidently type "make install" and install them system wide - -so don't. Really don't. -**Always compile libraries you want to have instrumented as static and link -these to the target program!** - -Then build the target. (Usually with `make`) - -**NOTES** - -1. sometimes configure and build systems are fickle and do not like - stderr output (and think this means a test failure) - which is something - AFL++ likes to do to show statistics. It is recommended to disable AFL++ - instrumentation reporting via `export AFL_QUIET=1`. - -2. sometimes configure and build systems error on warnings - these should be - disabled (e.g. `--disable-werror` for some configure scripts). - -3. in case the configure/build system complains about AFL++'s compiler and - aborts then set `export AFL_NOOPT=1` which will then just behave like the - real compiler. This option has to be unset again before building the target! - -##### configure - -For `configure` build systems this is usually done by: -`CC=afl-clang-fast CXX=afl-clang-fast++ ./configure --disable-shared` - -Note that if you are using the (better) afl-clang-lto compiler you also have to -set AR to llvm-ar[-VERSION] and RANLIB to llvm-ranlib[-VERSION] - as is -described in [instrumentation/README.lto.md](../instrumentation/README.lto.md). - -##### cmake - -For `cmake` build systems this is usually done by: -`mkdir build; cd build; cmake -DCMAKE_C_COMPILER=afl-cc -DCMAKE_CXX_COMPILER=afl-c++ ..` - -Note that if you are using the (better) afl-clang-lto compiler you also have to -set AR to llvm-ar[-VERSION] and RANLIB to llvm-ranlib[-VERSION] - as is -described in [instrumentation/README.lto.md](../instrumentation/README.lto.md). - -##### meson - -For meson you have to set the AFL++ compiler with the very first command! -`CC=afl-cc CXX=afl-c++ meson` - -##### other build systems or if configure/cmake didn't work - -Sometimes cmake and configure do not pick up the AFL++ compiler, or the -ranlib/ar that is needed - because this was just not foreseen by the developer -of the target. Or they have non-standard options. Figure out if there is a -non-standard way to set this, otherwise set up the build normally and edit the -generated build environment afterwards manually to point it to the right compiler -(and/or ranlib and ar). - -#### f) Better instrumentation - -If you just fuzz a target program as-is you are wasting a great opportunity for -much more fuzzing speed. - -This variant requires the usage of afl-clang-lto, afl-clang-fast or afl-gcc-fast. - -It is the so-called `persistent mode`, which is much, much faster but -requires that you code a source file that is specifically calling the target -functions that you want to fuzz, plus a few specific AFL++ functions around -it. See [instrumentation/README.persistent_mode.md](../instrumentation/README.persistent_mode.md) for details. - -Basically if you do not fuzz a target in persistent mode then you are just -doing it for a hobby and not professionally :-). - -#### g) libfuzzer fuzzer harnesses with LLVMFuzzerTestOneInput() - -libfuzzer `LLVMFuzzerTestOneInput()` harnesses are the defacto standard -for fuzzing, and they can be used with AFL++ (and honggfuzz) as well! -Compiling them is as simple as: -``` -afl-clang-fast++ -fsanitize=fuzzer -o harness harness.cpp targetlib.a -``` -You can even use advanced libfuzzer features like `FuzzedDataProvider`, -`LLVMFuzzerMutate()` etc. and they will work! - -The generated binary is fuzzed with afl-fuzz like any other fuzz target. - -Bonus: the target is already optimized for fuzzing due to persistent mode and -shared-memory testcases and hence gives you the fastest speed possible. - -For more information see [utils/aflpp_driver/README.md](../utils/aflpp_driver/README.md) - -### 2. Preparing the fuzzing campaign - -As you fuzz the target with mutated input, having as diverse inputs for the -target as possible improves the efficiency a lot. - -#### a) Collect inputs - -Try to gather valid inputs for the target from wherever you can. E.g. if it is -the PNG picture format try to find as many png files as possible, e.g. from -reported bugs, test suites, random downloads from the internet, unit test -case data - from all kind of PNG software. - -If the input format is not known, you can also modify a target program to write -normal data it receives and processes to a file and use these. - -#### b) Making the input corpus unique - -Use the AFL++ tool `afl-cmin` to remove inputs from the corpus that do not -produce a new path in the target. - -Put all files from step a) into one directory, e.g. INPUTS. - -If the target program is to be called by fuzzing as `bin/target -d INPUTFILE` -the run afl-cmin like this: -`afl-cmin -i INPUTS -o INPUTS_UNIQUE -- bin/target -d @@` -Note that the INPUTFILE argument that the target program would read from has to be set as `@@`. - -If the target reads from stdin instead, just omit the `@@` as this is the -default. - -This step is highly recommended! - -#### c) Minimizing all corpus files - -The shorter the input files that still traverse the same path -within the target, the better the fuzzing will be. This minimization -is done with `afl-tmin` however it is a long process as this has to -be done for every file: - -``` -mkdir input -cd INPUTS_UNIQUE -for i in *; do - afl-tmin -i "$i" -o "../input/$i" -- bin/target -d @@ -done -``` - -This step can also be parallelized, e.g. with `parallel`. -Note that this step is rather optional though. - -#### Done! - -The INPUTS_UNIQUE/ directory from step b) - or even better the directory input/ -if you minimized the corpus in step c) - is the resulting input corpus directory -to be used in fuzzing! :-) - -### 3. Fuzzing the target - -In this final step we fuzz the target. -There are not that many important options to run the target - unless you want -to use many CPU cores/threads for the fuzzing, which will make the fuzzing much -more useful. - -If you just use one CPU for fuzzing, then you are fuzzing just for fun and not -seriously :-) - -#### a) Running afl-fuzz - -Before you do even a test run of afl-fuzz execute `sudo afl-system-config` (on -the host if you execute afl-fuzz in a docker container). This reconfigures the -system for optimal speed - which afl-fuzz checks and bails otherwise. -Set `export AFL_SKIP_CPUFREQ=1` for afl-fuzz to skip this check if you cannot -run afl-system-config with root privileges on the host for whatever reason. - -Note there is also `sudo afl-persistent-config` which sets additional permanent -boot options for a much better fuzzing performance. - -Note that both scripts improve your fuzzing performance but also decrease your -system protection against attacks! So set strong firewall rules and only -expose SSH as a network service if you use these (which is highly recommended). - -If you have an input corpus from step 2 then specify this directory with the `-i` -option. Otherwise create a new directory and create a file with any content -as test data in there. - -If you do not want anything special, the defaults are already usually best, -hence all you need is to specify the seed input directory with the result of -step [2a. Collect inputs](#a-collect-inputs): -`afl-fuzz -i input -o output -- bin/target -d @@` -Note that the directory specified with -o will be created if it does not exist. - -It can be valuable to run afl-fuzz in a screen or tmux shell so you can log off, -or afl-fuzz is not aborted if you are running it in a remote ssh session where -the connection fails in between. -Only do that though once you have verified that your fuzzing setup works! -Simply run it like `screen -dmS afl-main -- afl-fuzz -M main-$HOSTNAME -i ...` -and it will start away in a screen session. To enter this session simply type -`screen -r afl-main`. You see - it makes sense to name the screen session -same as the afl-fuzz -M/-S naming :-) -For more information on screen or tmux please check their documentation. - -If you need to stop and re-start the fuzzing, use the same command line options -(or even change them by selecting a different power schedule or another -mutation mode!) and switch the input directory with a dash (`-`): -`afl-fuzz -i - -o output -- bin/target -d @@` - -Memory limits are not enforced by afl-fuzz by default and the system may run -out of memory. You can decrease the memory with the `-m` option, the value is -in MB. If this is too small for the target, you can usually see this by -afl-fuzz bailing with the message that it could not connect to the forkserver. - -Adding a dictionary is helpful. See the directory [dictionaries/](../dictionaries/) if -something is already included for your data format, and tell afl-fuzz to load -that dictionary by adding `-x dictionaries/FORMAT.dict`. With afl-clang-lto -you have an autodictionary generation for which you need to do nothing except -to use afl-clang-lto as the compiler. You also have the option to generate -a dictionary yourself, see [utils/libtokencap/README.md](../utils/libtokencap/README.md). - -afl-fuzz has a variety of options that help to workaround target quirks like -specific locations for the input file (`-f`), performing deterministic -fuzzing (`-D`) and many more. Check out `afl-fuzz -h`. - -We highly recommend that you set a memory limit for running the target with `-m` -which defines the maximum memory in MB. This prevents a potential -out-of-memory problem for your system plus helps you detect missing `malloc()` -failure handling in the target. -Play around with various -m values until you find one that safely works for all -your input seeds (if you have good ones and then double or quadrouple that. - -By default afl-fuzz never stops fuzzing. To terminate AFL++ simply press Control-C -or send a signal SIGINT. You can limit the number of executions or approximate runtime -in seconds with options also. - -When you start afl-fuzz you will see a user interface that shows what the status -is: - - -All labels are explained in [status_screen.md](status_screen.md). - -#### b) Using multiple cores - -If you want to seriously fuzz then use as many cores/threads as possible to -fuzz your target. - -On the same machine - due to the design of how AFL++ works - there is a maximum -number of CPU cores/threads that are useful, use more and the overall performance -degrades instead. This value depends on the target, and the limit is between 32 -and 64 cores per machine. - -If you have the RAM, it is highly recommended run the instances with a caching -of the testcases. Depending on the average testcase size (and those found -during fuzzing) and their number, a value between 50-500MB is recommended. -You can set the cache size (in MB) by setting the environment variable `AFL_TESTCACHE_SIZE`. - -There should be one main fuzzer (`-M main-$HOSTNAME` option) and as many secondary -fuzzers (eg `-S variant1`) as you have cores that you use. -Every -M/-S entry needs a unique name (that can be whatever), however the same --o output directory location has to be used for all instances. - -For every secondary fuzzer there should be a variation, e.g.: - * one should fuzz the target that was compiled differently: with sanitizers - activated (`export AFL_USE_ASAN=1 ; export AFL_USE_UBSAN=1 ; - export AFL_USE_CFISAN=1`) - * one or two should fuzz the target with CMPLOG/redqueen (see above), at - least one cmplog instance should follow transformations (`-l AT`) - * one to three fuzzers should fuzz a target compiled with laf-intel/COMPCOV - (see above). Important note: If you run more than one laf-intel/COMPCOV - fuzzer and you want them to share their intermediate results, the main - fuzzer (`-M`) must be one of the them! (Although this is not really - recommended.) - -All other secondaries should be used like this: - * A quarter to a third with the MOpt mutator enabled: `-L 0` - * run with a different power schedule, recommended are: - `fast (default), explore, coe, lin, quad, exploit and rare` - which you can set with e.g. `-p explore` - * a few instances should use the old queue cycling with `-Z` - -Also it is recommended to set `export AFL_IMPORT_FIRST=1` to load testcases -from other fuzzers in the campaign first. - -If you have a large corpus, a corpus from a previous run or are fuzzing in -a CI, then also set `export AFL_CMPLOG_ONLY_NEW=1` and `export AFL_FAST_CAL=1`. - -You can also use different fuzzers. -If you are using AFL spinoffs or AFL conforming fuzzers, then just use the -same -o directory and give it a unique `-S` name. -Examples are: - * [Fuzzolic](https://github.com/season-lab/fuzzolic) - * [symcc](https://github.com/eurecom-s3/symcc/) - * [Eclipser](https://github.com/SoftSec-KAIST/Eclipser/) - * [AFLsmart](https://github.com/aflsmart/aflsmart) - * [FairFuzz](https://github.com/carolemieux/afl-rb) - * [Neuzz](https://github.com/Dongdongshe/neuzz) - * [Angora](https://github.com/AngoraFuzzer/Angora) - -A long list can be found at [https://github.com/Microsvuln/Awesome-AFL](https://github.com/Microsvuln/Awesome-AFL) - -However you can also sync AFL++ with honggfuzz, libfuzzer with `-entropic=1`, etc. -Just show the main fuzzer (-M) with the `-F` option where the queue/work -directory of a different fuzzer is, e.g. `-F /src/target/honggfuzz`. -Using honggfuzz (with `-n 1` or `-n 2`) and libfuzzer in parallel is highly -recommended! - -#### c) Using multiple machines for fuzzing - -Maybe you have more than one machine you want to fuzz the same target on. -Simply start the `afl-fuzz` (and perhaps libfuzzer, honggfuzz, ...) -orchestra as you like, just ensure that your have one and only one `-M` -instance per server, and that its name is unique, hence the recommendation -for `-M main-$HOSTNAME`. - -Now there are three strategies on how you can sync between the servers: - * never: sounds weird, but this makes every server an island and has the - chance the each follow different paths into the target. You can make - this even more interesting by even giving different seeds to each server. - * regularly (~4h): this ensures that all fuzzing campaigns on the servers - "see" the same thing. It is like fuzzing on a huge server. - * in intervals of 1/10th of the overall expected runtime of the fuzzing you - sync. This tries a bit to combine both. have some individuality of the - paths each campaign on a server explores, on the other hand if one - gets stuck where another found progress this is handed over making it - unstuck. - -The syncing process itself is very simple. -As the `-M main-$HOSTNAME` instance syncs to all `-S` secondaries as well -as to other fuzzers, you have to copy only this directory to the other -machines. - -Lets say all servers have the `-o out` directory in /target/foo/out, and -you created a file `servers.txt` which contains the hostnames of all -participating servers, plus you have an ssh key deployed to all of them, -then run: -```bash -for FROM in `cat servers.txt`; do - for TO in `cat servers.txt`; do - rsync -rlpogtz --rsh=ssh $FROM:/target/foo/out/main-$FROM $TO:target/foo/out/ - done -done -``` -You can run this manually, per cron job - as you need it. -There is a more complex and configurable script in `utils/distributed_fuzzing`. - -#### d) The status of the fuzz campaign - -AFL++ comes with the `afl-whatsup` script to show the status of the fuzzing -campaign. - -Just supply the directory that afl-fuzz is given with the -o option and -you will see a detailed status of every fuzzer in that campaign plus -a summary. - -To have only the summary use the `-s` switch e.g.: `afl-whatsup -s out/` - -If you have multiple servers then use the command after a sync, or you have -to execute this script per server. - -Another tool to inspect the current state and history of a specific instance -is afl-plot, which generates an index.html file and a graphs that show how -the fuzzing instance is performing. -The syntax is `afl-plot instance_dir web_dir`, e.g. `afl-plot out/default /srv/www/htdocs/plot` - -#### e) Stopping fuzzing, restarting fuzzing, adding new seeds - -To stop an afl-fuzz run, simply press Control-C. - -To restart an afl-fuzz run, just reuse the same command line but replace the -`-i directory` with `-i -` or set `AFL_AUTORESUME=1`. - -If you want to add new seeds to a fuzzing campaign you can run a temporary -fuzzing instance, e.g. when your main fuzzer is using `-o out` and the new -seeds are in `newseeds/` directory: -``` -AFL_BENCH_JUST_ONE=1 AFL_FAST_CAL=1 afl-fuzz -i newseeds -o out -S newseeds -- ./target -``` - -#### f) Checking the coverage of the fuzzing - -The `paths found` value is a bad indicator for checking how good the coverage is. - -A better indicator - if you use default llvm instrumentation with at least -version 9 - is to use `afl-showmap` with the collect coverage option `-C` on -the output directory: -``` -$ afl-showmap -C -i out -o /dev/null -- ./target -params @@ -... -[*] Using SHARED MEMORY FUZZING feature. -[*] Target map size: 9960 -[+] Processed 7849 input files. -[+] Captured 4331 tuples (highest value 255, total values 67130596) in '/dev/nul -l'. -[+] A coverage of 4331 edges were achieved out of 9960 existing (43.48%) with 7849 input files. -``` -It is even better to check out the exact lines of code that have been reached - -and which have not been found so far. - -An "easy" helper script for this is [https://github.com/vanhauser-thc/afl-cov](https://github.com/vanhauser-thc/afl-cov), -just follow the README of that separate project. - -If you see that an important area or a feature has not been covered so far then -try to find an input that is able to reach that and start a new secondary in -that fuzzing campaign with that seed as input, let it run for a few minutes, -then terminate it. The main node will pick it up and make it available to the -other secondary nodes over time. Set `export AFL_NO_AFFINITY=1` or -`export AFL_TRY_AFFINITY=1` if you have no free core. - -Note that in nearly all cases you can never reach full coverage. A lot of -functionality is usually dependent on exclusive options that would need individual -fuzzing campaigns each with one of these options set. E.g. if you fuzz a library to -convert image formats and your target is the png to tiff API then you will not -touch any of the other library APIs and features. - -#### g) How long to fuzz a target? - -This is a difficult question. -Basically if no new path is found for a long time (e.g. for a day or a week) -then you can expect that your fuzzing won't be fruitful anymore. -However often this just means that you should switch out secondaries for -others, e.g. custom mutator modules, sync to very different fuzzers, etc. - -Keep the queue/ directory (for future fuzzings of the same or similar targets) -and use them to seed other good fuzzers like libfuzzer with the -entropic -switch or honggfuzz. - -#### h) Improve the speed! - - * Use [persistent mode](../instrumentation/README.persistent_mode.md) (x2-x20 speed increase) - * If you do not use shmem persistent mode, use `AFL_TMPDIR` to point the input file on a tempfs location, see [env_variables.md](env_variables.md) - * Linux: Improve kernel performance: modify `/etc/default/grub`, set `GRUB_CMDLINE_LINUX_DEFAULT="ibpb=off ibrs=off kpti=off l1tf=off mds=off mitigations=off no_stf_barrier noibpb noibrs nopcid nopti nospec_store_bypass_disable nospectre_v1 nospectre_v2 pcid=off pti=off spec_store_bypass_disable=off spectre_v2=off stf_barrier=off"`; then `update-grub` and `reboot` (warning: makes the system more insecure) - you can also just run `sudo afl-persistent-config` - * Linux: Running on an `ext2` filesystem with `noatime` mount option will be a bit faster than on any other journaling filesystem - * Use your cores! [b) Using multiple cores](#b-using-multiple-cores) - * Run `sudo afl-system-config` before starting the first afl-fuzz instance after a reboot - -### The End - -Check out the [FAQ](FAQ.md) if it maybe answers your question (that -you might not even have known you had ;-) ). - -This is basically all you need to know to professionally run fuzzing campaigns. -If you want to know more, the tons of texts in [docs/](./) will have you covered. - -Note that there are also a lot of tools out there that help fuzzing with AFL++ -(some might be deprecated or unsupported), see [tools.md](tools.md). \ No newline at end of file diff --git a/docs/fuzzing_in_depth.md b/docs/fuzzing_in_depth.md new file mode 100644 index 00000000..251bbc1d --- /dev/null +++ b/docs/fuzzing_in_depth.md @@ -0,0 +1,853 @@ +# Fuzzing with AFL++ + +The following describes how to fuzz with a target if source code is available. +If you have a binary-only target, please go to +[fuzzing_binary-only_targets.md](fuzzing_binary-only_targets.md). + +Fuzzing source code is a three-step process: + +1. Compile the target with a special compiler that prepares the target to be + fuzzed efficiently. This step is called "instrumenting a target". +2. Prepare the fuzzing by selecting and optimizing the input corpus for the + target. +3. Perform the fuzzing of the target by randomly mutating input and assessing if + a generated input was processed in a new path in the target binary. + +## 0. Common sense risks + +Please keep in mind that, similarly to many other computationally-intensive +tasks, fuzzing may put a strain on your hardware and on the OS. In particular: + +- Your CPU will run hot and will need adequate cooling. In most cases, if + cooling is insufficient or stops working properly, CPU speeds will be + automatically throttled. That said, especially when fuzzing on less suitable + hardware (laptops, smartphones, etc.), it's not entirely impossible for + something to blow up. + +- Targeted programs may end up erratically grabbing gigabytes of memory or + filling up disk space with junk files. AFL++ tries to enforce basic memory + limits, but can't prevent each and every possible mishap. The bottom line is + that you shouldn't be fuzzing on systems where the prospect of data loss is + not an acceptable risk. + +- Fuzzing involves billions of reads and writes to the filesystem. On modern + systems, this will be usually heavily cached, resulting in fairly modest + "physical" I/O - but there are many factors that may alter this equation. It + is your responsibility to monitor for potential trouble; with very heavy I/O, + the lifespan of many HDDs and SSDs may be reduced. + + A good way to monitor disk I/O on Linux is the `iostat` command: + + ```shell + $ iostat -d 3 -x -k [...optional disk ID...] + ``` + + Using the `AFL_TMPDIR` environment variable and a RAM-disk, you can have the + heavy writing done in RAM to prevent the aforementioned wear and tear. For + example, the following line will run a Docker container with all this preset: + + ```shell + # docker run -ti --mount type=tmpfs,destination=/ramdisk -e AFL_TMPDIR=/ramdisk aflplusplus/aflplusplus + ``` + +## 1. Instrumenting the target + +### a) Selecting the best AFL++ compiler for instrumenting the target + +AFL++ comes with a central compiler `afl-cc` that incorporates various different +kinds of compiler targets and and instrumentation options. The following +evaluation flow will help you to select the best possible. + +It is highly recommended to have the newest llvm version possible installed, +anything below 9 is not recommended. + +``` ++--------------------------------+ +| clang/clang++ 11+ is available | --> use LTO mode (afl-clang-lto/afl-clang-lto++) ++--------------------------------+ see [instrumentation/README.lto.md](instrumentation/README.lto.md) + | + | if not, or if the target fails with LTO afl-clang-lto/++ + | + v ++---------------------------------+ +| clang/clang++ 3.8+ is available | --> use LLVM mode (afl-clang-fast/afl-clang-fast++) ++---------------------------------+ see [instrumentation/README.llvm.md](instrumentation/README.llvm.md) + | + | if not, or if the target fails with LLVM afl-clang-fast/++ + | + v + +--------------------------------+ + | gcc 5+ is available | -> use GCC_PLUGIN mode (afl-gcc-fast/afl-g++-fast) + +--------------------------------+ see [instrumentation/README.gcc_plugin.md](instrumentation/README.gcc_plugin.md) and + [instrumentation/README.instrument_list.md](instrumentation/README.instrument_list.md) + | + | if not, or if you do not have a gcc with plugin support + | + v + use GCC mode (afl-gcc/afl-g++) (or afl-clang/afl-clang++ for clang) +``` + +Clickable README links for the chosen compiler: + +* [LTO mode - afl-clang-lto](../instrumentation/README.lto.md) +* [LLVM mode - afl-clang-fast](../instrumentation/README.llvm.md) +* [GCC_PLUGIN mode - afl-gcc-fast](../instrumentation/README.gcc_plugin.md) +* GCC/CLANG modes (afl-gcc/afl-clang) have no README as they have no own + features + +You can select the mode for the afl-cc compiler by: +1. use a symlink to afl-cc: afl-gcc, afl-g++, afl-clang, afl-clang++, + afl-clang-fast, afl-clang-fast++, afl-clang-lto, afl-clang-lto++, + afl-gcc-fast, afl-g++-fast (recommended!) +2. using the environment variable AFL_CC_COMPILER with MODE +3. passing --afl-MODE command line options to the compiler via + CFLAGS/CXXFLAGS/CPPFLAGS + +MODE can be one of: LTO (afl-clang-lto*), LLVM (afl-clang-fast*), GCC_PLUGIN +(afl-g*-fast) or GCC (afl-gcc/afl-g++) or CLANG(afl-clang/afl-clang++). + +Because no AFL specific command-line options are accepted (beside the --afl-MODE +command), the compile-time tools make fairly broad use of environment variables, +which can be listed with `afl-cc -hh` or by reading +[env_variables.md](env_variables.md). + +### b) Selecting instrumentation options + +The following options are available when you instrument with LTO mode +(afl-clang-fast/afl-clang-lto): + +* Splitting integer, string, float and switch comparisons so AFL++ can easier + solve these. This is an important option if you do not have a very good and + large input corpus. This technique is called laf-intel or COMPCOV. To use this + set the following environment variable before compiling the target: `export + AFL_LLVM_LAF_ALL=1` You can read more about this in + [instrumentation/README.laf-intel.md](../instrumentation/README.laf-intel.md). +* A different technique (and usually a better one than laf-intel) is to + instrument the target so that any compare values in the target are sent to + AFL++ which then tries to put these values into the fuzzing data at different + locations. This technique is very fast and good - if the target does not + transform input data before comparison. Therefore this technique is called + `input to state` or `redqueen`. If you want to use this technique, then you + have to compile the target twice, once specifically with/for this mode by + setting `AFL_LLVM_CMPLOG=1`, and pass this binary to afl-fuzz via the `-c` + parameter. Note that you can compile also just a cmplog binary and use that + for both however there will be a performance penality. You can read more about + this in + [instrumentation/README.cmplog.md](../instrumentation/README.cmplog.md). + +If you use LTO, LLVM or GCC_PLUGIN mode +(afl-clang-fast/afl-clang-lto/afl-gcc-fast) you have the option to selectively +only instrument parts of the target that you are interested in: + +* To instrument only those parts of the target that you are interested in create + a file with all the filenames of the source code that should be instrumented. + For afl-clang-lto and afl-gcc-fast - or afl-clang-fast if a mode other than + DEFAULT/PCGUARD is used or you have llvm > 10.0.0 - just put one filename or + function per line (no directory information necessary for filenames9, and + either set `export AFL_LLVM_ALLOWLIST=allowlist.txt` **or** `export + AFL_LLVM_DENYLIST=denylist.txt` - depending on if you want per default to + instrument unless noted (DENYLIST) or not perform instrumentation unless + requested (ALLOWLIST). **NOTE:** During optimization functions might be + inlined and then would not match! See + [instrumentation/README.instrument_list.md](../instrumentation/README.instrument_list.md) + +There are many more options and modes available however these are most of the +time less effective. See: +* [instrumentation/README.ctx.md](../instrumentation/README.ctx.md) +* [instrumentation/README.ngram.md](../instrumentation/README.ngram.md) + +AFL++ performs "never zero" counting in its bitmap. You can read more about this +here: +* [instrumentation/README.neverzero.md](../instrumentation/README.neverzero.md) + +### c) Selecting sanitizers + +It is possible to use sanitizers when instrumenting targets for fuzzing, which +allows you to find bugs that would not necessarily result in a crash. + +Note that sanitizers have a huge impact on CPU (= less executions per second) +and RAM usage. Also you should only run one afl-fuzz instance per sanitizer +type. This is enough because a use-after-free bug will be picked up, e.g. by +ASAN (address sanitizer) anyway when syncing to other fuzzing instances, so not +all fuzzing instances need to be instrumented with ASAN. + +The following sanitizers have built-in support in AFL++: +* ASAN = Address SANitizer, finds memory corruption vulnerabilities like + use-after-free, NULL pointer dereference, buffer overruns, etc. Enabled with + `export AFL_USE_ASAN=1` before compiling. +* MSAN = Memory SANitizer, finds read access to uninitialized memory, eg. a + local variable that is defined and read before it is even set. Enabled with + `export AFL_USE_MSAN=1` before compiling. +* UBSAN = Undefined Behaviour SANitizer, finds instances where - by the C and + C++ standards - undefined behaviour happens, e.g. adding two signed integers + together where the result is larger than a signed integer can hold. Enabled + with `export AFL_USE_UBSAN=1` before compiling. +* CFISAN = Control Flow Integrity SANitizer, finds instances where the control + flow is found to be illegal. Originally this was rather to prevent return + oriented programming exploit chains from functioning, in fuzzing this is + mostly reduced to detecting type confusion vulnerabilities - which is, + however, one of the most important and dangerous C++ memory corruption + classes! Enabled with `export AFL_USE_CFISAN=1` before compiling. +* TSAN = Thread SANitizer, finds thread race conditions. Enabled with `export + AFL_USE_TSAN=1` before compiling. +* LSAN = Leak SANitizer, finds memory leaks in a program. This is not really a + security issue, but for developers this can be very valuable. Note that unlike + the other sanitizers above this needs `__AFL_LEAK_CHECK();` added to all areas + of the target source code where you find a leak check necessary! Enabled with + `export AFL_USE_LSAN=1` before compiling. + +It is possible to further modify the behaviour of the sanitizers at run-time by +setting `ASAN_OPTIONS=...`, `LSAN_OPTIONS` etc. - the available parameters can +be looked up in the sanitizer documentation of llvm/clang. afl-fuzz, however, +requires some specific parameters important for fuzzing to be set. If you want +to set your own, it might bail and report what it is missing. + +Note that some sanitizers cannot be used together, e.g. ASAN and MSAN, and +others often cannot work together because of target weirdness, e.g. ASAN and +CFISAN. You might need to experiment which sanitizers you can combine in a +target (which means more instances can be run without a sanitized target, which +is more effective). + +### d) Modifying the target + +If the target has features that make fuzzing more difficult, e.g. checksums, +HMAC, etc. then modify the source code so that checks for these values are +removed. This can even be done safely for source code used in operational +products by eliminating these checks within these AFL specific blocks: + +``` +#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION + // say that the checksum or HMAC was fine - or whatever is required + // to eliminate the need for the fuzzer to guess the right checksum + return 0; +#endif +``` + +All AFL++ compilers will set this preprocessor definition automatically. + +### e) Instrumenting the target + +In this step the target source code is compiled so that it can be fuzzed. + +Basically you have to tell the target build system that the selected AFL++ +compiler is used. Also - if possible - you should always configure the build +system such that the target is compiled statically and not dynamically. How to +do this is described below. + +The #1 rule when instrumenting a target is: avoid instrumenting shared libraries +at all cost. You would need to set LD_LIBRARY_PATH to point to these, you could +accidentally type "make install" and install them system wide - so don't. Really +don't. **Always compile libraries you want to have instrumented as static and +link these to the target program!** + +Then build the target. (Usually with `make`) + +**NOTES** + +1. sometimes configure and build systems are fickle and do not like stderr + output (and think this means a test failure) - which is something AFL++ likes + to do to show statistics. It is recommended to disable AFL++ instrumentation + reporting via `export AFL_QUIET=1`. + +2. sometimes configure and build systems error on warnings - these should be + disabled (e.g. `--disable-werror` for some configure scripts). + +3. in case the configure/build system complains about AFL++'s compiler and + aborts then set `export AFL_NOOPT=1` which will then just behave like the + real compiler. This option has to be unset again before building the target! + +#### configure + +For `configure` build systems this is usually done by: +`CC=afl-clang-fast CXX=afl-clang-fast++ ./configure --disable-shared` + +Note that if you are using the (better) afl-clang-lto compiler you also have to +set AR to llvm-ar[-VERSION] and RANLIB to llvm-ranlib[-VERSION] - as is +described in [instrumentation/README.lto.md](../instrumentation/README.lto.md). + +#### cmake + +For `cmake` build systems this is usually done by: +`mkdir build; cd build; cmake -DCMAKE_C_COMPILER=afl-cc -DCMAKE_CXX_COMPILER=afl-c++ ..` + +Note that if you are using the (better) afl-clang-lto compiler you also have to +set AR to llvm-ar[-VERSION] and RANLIB to llvm-ranlib[-VERSION] - as is +described in [instrumentation/README.lto.md](../instrumentation/README.lto.md). + +#### meson + +For meson you have to set the AFL++ compiler with the very first command! +`CC=afl-cc CXX=afl-c++ meson` + +#### other build systems or if configure/cmake didn't work + +Sometimes cmake and configure do not pick up the AFL++ compiler, or the +ranlib/ar that is needed - because this was just not foreseen by the developer +of the target. Or they have non-standard options. Figure out if there is a +non-standard way to set this, otherwise set up the build normally and edit the +generated build environment afterwards manually to point it to the right +compiler (and/or ranlib and ar). + +### f) Better instrumentation + +If you just fuzz a target program as-is you are wasting a great opportunity for +much more fuzzing speed. + +This variant requires the usage of afl-clang-lto, afl-clang-fast or +afl-gcc-fast. + +It is the so-called `persistent mode`, which is much, much faster but requires +that you code a source file that is specifically calling the target functions +that you want to fuzz, plus a few specific AFL++ functions around it. See +[instrumentation/README.persistent_mode.md](../instrumentation/README.persistent_mode.md) +for details. + +Basically if you do not fuzz a target in persistent mode then you are just doing +it for a hobby and not professionally :-). + +### g) libfuzzer fuzzer harnesses with LLVMFuzzerTestOneInput() + +libfuzzer `LLVMFuzzerTestOneInput()` harnesses are the defacto standard +for fuzzing, and they can be used with AFL++ (and honggfuzz) as well! + +Compiling them is as simple as: + +``` +afl-clang-fast++ -fsanitize=fuzzer -o harness harness.cpp targetlib.a +``` + +You can even use advanced libfuzzer features like `FuzzedDataProvider`, +`LLVMFuzzerMutate()` etc. and they will work! + +The generated binary is fuzzed with afl-fuzz like any other fuzz target. + +Bonus: the target is already optimized for fuzzing due to persistent mode and +shared-memory test cases and hence gives you the fastest speed possible. + +For more information, see +[utils/aflpp_driver/README.md](../utils/aflpp_driver/README.md). + +## 2. Preparing the fuzzing campaign + +As you fuzz the target with mutated input, having as diverse inputs for the +target as possible improves the efficiency a lot. + +### a) Collecting inputs + +To operate correctly, the fuzzer requires one or more starting files that +contain a good example of the input data normally expected by the targeted +application. + +Try to gather valid inputs for the target from wherever you can. E.g., if it is +the PNG picture format, try to find as many PNG files as possible, e.g., from +reported bugs, test suites, random downloads from the internet, unit test case +data - from all kind of PNG software. + +If the input format is not known, you can also modify a target program to write +normal data it receives and processes to a file and use these. + +You can find many good examples of starting files in the +[testcases/](../testcases) subdirectory that comes with this tool. + +### b) Making the input corpus unique + +Use the AFL++ tool `afl-cmin` to remove inputs from the corpus that do not +produce a new path in the target. + +Put all files from step a) into one directory, e.g. INPUTS. + +If the target program is to be called by fuzzing as `bin/target -d INPUTFILE` +the run afl-cmin like this: +`afl-cmin -i INPUTS -o INPUTS_UNIQUE -- bin/target -d @@` +Note that the INPUTFILE argument that the target program would read from has to be set as `@@`. + +If the target reads from stdin instead, just omit the `@@` as this is the +default. + +This step is highly recommended! + +### c) Minimizing all corpus files + +The shorter the input files that still traverse the same path within the target, +the better the fuzzing will be. This minimization is done with `afl-tmin` +however it is a long process as this has to be done for every file: + +``` +mkdir input +cd INPUTS_UNIQUE +for i in *; do + afl-tmin -i "$i" -o "../input/$i" -- bin/target -d @@ +done +``` + +This step can also be parallelized, e.g. with `parallel`. Note that this step is +rather optional though. + +### Done! + +The INPUTS_UNIQUE/ directory from step b) - or even better the directory input/ +if you minimized the corpus in step c) - is the resulting input corpus directory +to be used in fuzzing! :-) + +## 3. Fuzzing the target + +In this final step we fuzz the target. There are not that many important options +to run the target - unless you want to use many CPU cores/threads for the +fuzzing, which will make the fuzzing much more useful. + +If you just use one CPU for fuzzing, then you are fuzzing just for fun and not +seriously :-) + +### a) Running afl-fuzz + +Before you do even a test run of afl-fuzz execute `sudo afl-system-config` (on +the host if you execute afl-fuzz in a docker container). This reconfigures the +system for optimal speed - which afl-fuzz checks and bails otherwise. Set +`export AFL_SKIP_CPUFREQ=1` for afl-fuzz to skip this check if you cannot run +afl-system-config with root privileges on the host for whatever reason. + +Note there is also `sudo afl-persistent-config` which sets additional permanent +boot options for a much better fuzzing performance. + +Note that both scripts improve your fuzzing performance but also decrease your +system protection against attacks! So set strong firewall rules and only expose +SSH as a network service if you use these (which is highly recommended). + +If you have an input corpus from step 2 then specify this directory with the +`-i` option. Otherwise create a new directory and create a file with any content +as test data in there. + +If you do not want anything special, the defaults are already usually best, +hence all you need is to specify the seed input directory with the result of +step [2a) Collect inputs](#a-collect-inputs): +`afl-fuzz -i input -o output -- bin/target -d @@` +Note that the directory specified with -o will be created if it does not exist. + +It can be valuable to run afl-fuzz in a screen or tmux shell so you can log off, +or afl-fuzz is not aborted if you are running it in a remote ssh session where +the connection fails in between. +Only do that though once you have verified that your fuzzing setup works! +Simply run it like `screen -dmS afl-main -- afl-fuzz -M main-$HOSTNAME -i ...` +and it will start away in a screen session. To enter this session simply type +`screen -r afl-main`. You see - it makes sense to name the screen session +same as the afl-fuzz -M/-S naming :-) +For more information on screen or tmux please check their documentation. + +If you need to stop and re-start the fuzzing, use the same command line options +(or even change them by selecting a different power schedule or another mutation +mode!) and switch the input directory with a dash (`-`): +`afl-fuzz -i - -o output -- bin/target -d @@` + +Adding a dictionary is helpful. See the directory +[dictionaries/](../dictionaries/) if something is already included for your data +format, and tell afl-fuzz to load that dictionary by adding `-x +dictionaries/FORMAT.dict`. With afl-clang-lto you have an autodictionary +generation for which you need to do nothing except to use afl-clang-lto as the +compiler. You also have the option to generate a dictionary yourself, see +[utils/libtokencap/README.md](../utils/libtokencap/README.md). + +afl-fuzz has a variety of options that help to workaround target quirks like +specific locations for the input file (`-f`), performing deterministic fuzzing +(`-D`) and many more. Check out `afl-fuzz -h`. + +We highly recommend that you set a memory limit for running the target with `-m` +which defines the maximum memory in MB. This prevents a potential out-of-memory +problem for your system plus helps you detect missing `malloc()` failure +handling in the target. Play around with various -m values until you find one +that safely works for all your input seeds (if you have good ones and then +double or quadruple that. + +By default afl-fuzz never stops fuzzing. To terminate AFL++ simply press +Control-C or send a signal SIGINT. You can limit the number of executions or +approximate runtime in seconds with options also. + +When you start afl-fuzz you will see a user interface that shows what the status +is: + + +All labels are explained in [status_screen.md](status_screen.md). + +### b) Keeping memory use and timeouts in check + +Memory limits are not enforced by afl-fuzz by default and the system may run out +of memory. You can decrease the memory with the `-m` option, the value is in MB. +If this is too small for the target, you can usually see this by afl-fuzz +bailing with the message that it could not connect to the forkserver. + +Consider setting low values for `-m` and `-t`. + +For programs that are nominally very fast, but get sluggish for some inputs, you +can also try setting `-t` values that are more punishing than what `afl-fuzz` +dares to use on its own. On fast and idle machines, going down to `-t 5` may be +a viable plan. + +The `-m` parameter is worth looking at, too. Some programs can end up spending a +fair amount of time allocating and initializing megabytes of memory when +presented with pathological inputs. Low `-m` values can make them give up sooner +and not waste CPU time. + +### c) Using multiple cores + +If you want to seriously fuzz then use as many cores/threads as possible to fuzz +your target. + +On the same machine - due to the design of how AFL++ works - there is a maximum +number of CPU cores/threads that are useful, use more and the overall +performance degrades instead. This value depends on the target, and the limit is +between 32 and 64 cores per machine. + +If you have the RAM, it is highly recommended run the instances with a caching +of the test cases. Depending on the average test case size (and those found +during fuzzing) and their number, a value between 50-500MB is recommended. You +can set the cache size (in MB) by setting the environment variable +`AFL_TESTCACHE_SIZE`. + +There should be one main fuzzer (`-M main-$HOSTNAME` option) and as many +secondary fuzzers (e.g. `-S variant1`) as you have cores that you use. Every +-M/-S entry needs a unique name (that can be whatever), however, the same -o +output directory location has to be used for all instances. + +For every secondary fuzzer there should be a variation, e.g.: +* one should fuzz the target that was compiled differently: with sanitizers + activated (`export AFL_USE_ASAN=1 ; export AFL_USE_UBSAN=1 ; export + AFL_USE_CFISAN=1`) +* one or two should fuzz the target with CMPLOG/redqueen (see above), at least + one cmplog instance should follow transformations (`-l AT`) +* one to three fuzzers should fuzz a target compiled with laf-intel/COMPCOV (see + above). Important note: If you run more than one laf-intel/COMPCOV fuzzer and + you want them to share their intermediate results, the main fuzzer (`-M`) must + be one of them! (Although this is not really recommended.) + +All other secondaries should be used like this: +* a quarter to a third with the MOpt mutator enabled: `-L 0` +* run with a different power schedule, recommended are: + `fast (default), explore, coe, lin, quad, exploit and rare` which you can set + with e.g. `-p explore` +* a few instances should use the old queue cycling with `-Z` + +Also, it is recommended to set `export AFL_IMPORT_FIRST=1` to load test cases +from other fuzzers in the campaign first. + +If you have a large corpus, a corpus from a previous run or are fuzzing in +a CI, then also set `export AFL_CMPLOG_ONLY_NEW=1` and `export AFL_FAST_CAL=1`. + +You can also use different fuzzers. If you are using AFL spinoffs or AFL +conforming fuzzers, then just use the same -o directory and give it a unique +`-S` name. Examples are: +* [Fuzzolic](https://github.com/season-lab/fuzzolic) +* [symcc](https://github.com/eurecom-s3/symcc/) +* [Eclipser](https://github.com/SoftSec-KAIST/Eclipser/) +* [AFLsmart](https://github.com/aflsmart/aflsmart) +* [FairFuzz](https://github.com/carolemieux/afl-rb) +* [Neuzz](https://github.com/Dongdongshe/neuzz) +* [Angora](https://github.com/AngoraFuzzer/Angora) + +A long list can be found at +[https://github.com/Microsvuln/Awesome-AFL](https://github.com/Microsvuln/Awesome-AFL). + +However, you can also sync AFL++ with honggfuzz, libfuzzer with `-entropic=1`, +etc. Just show the main fuzzer (-M) with the `-F` option where the queue/work +directory of a different fuzzer is, e.g. `-F /src/target/honggfuzz`. Using +honggfuzz (with `-n 1` or `-n 2`) and libfuzzer in parallel is highly +recommended! + +### d) Using multiple machines for fuzzing + +Maybe you have more than one machine you want to fuzz the same target on. +Simply start the `afl-fuzz` (and perhaps libfuzzer, honggfuzz, ...) +orchestra as you like, just ensure that your have one and only one `-M` +instance per server, and that its name is unique, hence the recommendation +for `-M main-$HOSTNAME`. + +Now there are three strategies on how you can sync between the servers: +* never: sounds weird, but this makes every server an island and has the chance + the each follow different paths into the target. You can make this even more + interesting by even giving different seeds to each server. +* regularly (~4h): this ensures that all fuzzing campaigns on the servers "see" + the same thing. It is like fuzzing on a huge server. +* in intervals of 1/10th of the overall expected runtime of the fuzzing you + sync. This tries a bit to combine both. have some individuality of the paths + each campaign on a server explores, on the other hand if one gets stuck where + another found progress this is handed over making it unstuck. + +The syncing process itself is very simple. As the `-M main-$HOSTNAME` instance +syncs to all `-S` secondaries as well as to other fuzzers, you have to copy only +this directory to the other machines. + +Lets say all servers have the `-o out` directory in /target/foo/out, and you +created a file `servers.txt` which contains the hostnames of all participating +servers, plus you have an ssh key deployed to all of them, then run: + +```bash +for FROM in `cat servers.txt`; do + for TO in `cat servers.txt`; do + rsync -rlpogtz --rsh=ssh $FROM:/target/foo/out/main-$FROM $TO:target/foo/out/ + done +done +``` + +You can run this manually, per cron job - as you need it. There is a more +complex and configurable script in `utils/distributed_fuzzing`. + +### e) The status of the fuzz campaign + +AFL++ comes with the `afl-whatsup` script to show the status of the fuzzing +campaign. + +Just supply the directory that afl-fuzz is given with the `-o` option and you +will see a detailed status of every fuzzer in that campaign plus a summary. + +To have only the summary, use the `-s` switch, e.g., `afl-whatsup -s out/`. + +If you have multiple servers, then use the command after a sync or you have to +execute this script per server. + +Another tool to inspect the current state and history of a specific instance is +afl-plot, which generates an index.html file and a graphs that show how the +fuzzing instance is performing. The syntax is `afl-plot instance_dir web_dir`, +e.g., `afl-plot out/default /srv/www/htdocs/plot`. + +### f) Stopping fuzzing, restarting fuzzing, adding new seeds + +To stop an afl-fuzz run, simply press Control-C. + +To restart an afl-fuzz run, just reuse the same command line but replace the `-i +directory` with `-i -` or set `AFL_AUTORESUME=1`. + +If you want to add new seeds to a fuzzing campaign you can run a temporary +fuzzing instance, e.g. when your main fuzzer is using `-o out` and the new seeds +are in `newseeds/` directory: + +``` +AFL_BENCH_JUST_ONE=1 AFL_FAST_CAL=1 afl-fuzz -i newseeds -o out -S newseeds -- ./target +``` + +### g) Checking the coverage of the fuzzing + +The `paths found` value is a bad indicator for checking how good the coverage +is. + +A better indicator - if you use default llvm instrumentation with at least +version 9 - is to use `afl-showmap` with the collect coverage option `-C` on the +output directory: + +``` +$ afl-showmap -C -i out -o /dev/null -- ./target -params @@ +... +[*] Using SHARED MEMORY FUZZING feature. +[*] Target map size: 9960 +[+] Processed 7849 input files. +[+] Captured 4331 tuples (highest value 255, total values 67130596) in '/dev/nul +l'. +[+] A coverage of 4331 edges were achieved out of 9960 existing (43.48%) with 7849 input files. +``` + +It is even better to check out the exact lines of code that have been reached - +and which have not been found so far. + +An "easy" helper script for this is +[https://github.com/vanhauser-thc/afl-cov](https://github.com/vanhauser-thc/afl-cov), +just follow the README of that separate project. + +If you see that an important area or a feature has not been covered so far then +try to find an input that is able to reach that and start a new secondary in +that fuzzing campaign with that seed as input, let it run for a few minutes, +then terminate it. The main node will pick it up and make it available to the +other secondary nodes over time. Set `export AFL_NO_AFFINITY=1` or `export +AFL_TRY_AFFINITY=1` if you have no free core. + +Note that in nearly all cases you can never reach full coverage. A lot of +functionality is usually dependent on exclusive options that would need +individual fuzzing campaigns each with one of these options set. E.g., if you +fuzz a library to convert image formats and your target is the png to tiff API +then you will not touch any of the other library APIs and features. + +### h) How long to fuzz a target? + +This is a difficult question. Basically if no new path is found for a long time +(e.g. for a day or a week) then you can expect that your fuzzing won't be +fruitful anymore. However, often this just means that you should switch out +secondaries for others, e.g. custom mutator modules, sync to very different +fuzzers, etc. + +Keep the queue/ directory (for future fuzzings of the same or similar targets) +and use them to seed other good fuzzers like libfuzzer with the -entropic switch +or honggfuzz. + +### i) Improve the speed! + +* Use [persistent mode](../instrumentation/README.persistent_mode.md) (x2-x20 + speed increase) +* If you do not use shmem persistent mode, use `AFL_TMPDIR` to point the input + file on a tempfs location, see [env_variables.md](env_variables.md) +* Linux: Improve kernel performance: modify `/etc/default/grub`, set + `GRUB_CMDLINE_LINUX_DEFAULT="ibpb=off ibrs=off kpti=off l1tf=off mds=off + mitigations=off no_stf_barrier noibpb noibrs nopcid nopti + nospec_store_bypass_disable nospectre_v1 nospectre_v2 pcid=off pti=off + spec_store_bypass_disable=off spectre_v2=off stf_barrier=off"`; then + `update-grub` and `reboot` (warning: makes the system more insecure) - you can + also just run `sudo afl-persistent-config` +* Linux: Running on an `ext2` filesystem with `noatime` mount option will be a + bit faster than on any other journaling filesystem +* Use your cores! [3c) Using multiple cores](#c-using-multiple-cores) +* Run `sudo afl-system-config` before starting the first afl-fuzz instance after + a reboot + +### j) Going beyond crashes + +Fuzzing is a wonderful and underutilized technique for discovering non-crashing +design and implementation errors, too. Quite a few interesting bugs have been +found by modifying the target programs to call `abort()` when say: + +- Two bignum libraries produce different outputs when given the same + fuzzer-generated input. + +- An image library produces different outputs when asked to decode the same + input image several times in a row. + +- A serialization/deserialization library fails to produce stable outputs when + iteratively serializing and deserializing fuzzer-supplied data. + +- A compression library produces an output inconsistent with the input file when + asked to compress and then decompress a particular blob. + +Implementing these or similar sanity checks usually takes very little time; if +you are the maintainer of a particular package, you can make this code +conditional with `#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION` (a flag also +shared with libfuzzer and honggfuzz) or `#ifdef __AFL_COMPILER` (this one is +just for AFL++). + +### k) Known limitations & areas for improvement + +Here are some of the most important caveats for AFL++: + +- AFL++ detects faults by checking for the first spawned process dying due to a + signal (SIGSEGV, SIGABRT, etc). Programs that install custom handlers for + these signals may need to have the relevant code commented out. In the same + vein, faults in child processes spawned by the fuzzed target may evade + detection unless you manually add some code to catch that. + +- As with any other brute-force tool, the fuzzer offers limited coverage if + encryption, checksums, cryptographic signatures, or compression are used to + wholly wrap the actual data format to be tested. + + To work around this, you can comment out the relevant checks (see + utils/libpng_no_checksum/ for inspiration); if this is not possible, you can + also write a postprocessor, one of the hooks of custom mutators. See + [custom_mutators.md](custom_mutators.md) on how to use + `AFL_CUSTOM_MUTATOR_LIBRARY`. + +- There are some unfortunate trade-offs with ASAN and 64-bit binaries. This + isn't due to any specific fault of afl-fuzz. + +- There is no direct support for fuzzing network services, background daemons, + or interactive apps that require UI interaction to work. You may need to make + simple code changes to make them behave in a more traditional way. Preeny may + offer a relatively simple option, too - see: + [https://github.com/zardus/preeny](https://github.com/zardus/preeny) + + Some useful tips for modifying network-based services can be also found at: + [https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop](https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop) + +- Occasionally, sentient machines rise against their creators. If this happens + to you, please consult + [https://lcamtuf.coredump.cx/prep/](https://lcamtuf.coredump.cx/prep/). + +Beyond this, see [INSTALL.md](INSTALL.md) for platform-specific tips. + +## 4. Triaging crashes + +The coverage-based grouping of crashes usually produces a small data set that +can be quickly triaged manually or with a very simple GDB or Valgrind script. +Every crash is also traceable to its parent non-crashing test case in the queue, +making it easier to diagnose faults. + +Having said that, it's important to acknowledge that some fuzzing crashes can be +difficult to quickly evaluate for exploitability without a lot of debugging and +code analysis work. To assist with this task, afl-fuzz supports a very unique +"crash exploration" mode enabled with the -C flag. + +In this mode, the fuzzer takes one or more crashing test cases as the input and +uses its feedback-driven fuzzing strategies to very quickly enumerate all code +paths that can be reached in the program while keeping it in the crashing state. + +Mutations that do not result in a crash are rejected; so are any changes that do +not affect the execution path. + +The output is a small corpus of files that can be very rapidly examined to see +what degree of control the attacker has over the faulting address, or whether it +is possible to get past an initial out-of-bounds read - and see what lies +beneath. + +Oh, one more thing: for test case minimization, give afl-tmin a try. The tool +can be operated in a very simple way: + +```shell +./afl-tmin -i test_case -o minimized_result -- /path/to/program [...] +``` + +The tool works with crashing and non-crashing test cases alike. In the crash +mode, it will happily accept instrumented and non-instrumented binaries. In the +non-crashing mode, the minimizer relies on standard AFL++ instrumentation to +make the file simpler without altering the execution path. + +The minimizer accepts the -m, -t, -f and @@ syntax in a manner compatible with +afl-fuzz. + +Another tool in AFL++ is the afl-analyze tool. It takes an input file, attempts +to sequentially flip bytes, and observes the behavior of the tested program. It +then color-codes the input based on which sections appear to be critical, and +which are not; while not bulletproof, it can often offer quick insights into +complex file formats. + + +## 5. CI fuzzing + +Some notes on CI fuzzing - this fuzzing is different to normal fuzzing campaigns +as these are much shorter runnings. + +1. Always: + * LTO has a much longer compile time which is diametrical to short fuzzing - + hence use afl-clang-fast instead. + * If you compile with CMPLOG, then you can save fuzzing time and reuse that + compiled target for both the `-c` option and the main fuzz target. This + will impact the speed by ~15% though. + * `AFL_FAST_CAL` - Enable fast calibration, this halves the time the + saturated corpus needs to be loaded. + * `AFL_CMPLOG_ONLY_NEW` - only perform cmplog on new found paths, not the + initial corpus as this very likely has been done for them already. + * Keep the generated corpus, use afl-cmin and reuse it every time! + +2. Additionally randomize the AFL++ compilation options, e.g.: + * 40% for `AFL_LLVM_CMPLOG` + * 10% for `AFL_LLVM_LAF_ALL` + +3. Also randomize the afl-fuzz runtime options, e.g.: + * 65% for `AFL_DISABLE_TRIM` + * 50% use a dictionary generated by `AFL_LLVM_DICT2FILE` + * 40% use MOpt (`-L 0`) + * 40% for `AFL_EXPAND_HAVOC_NOW` + * 20% for old queue processing (`-Z`) + * for CMPLOG targets, 60% for `-l 2`, 40% for `-l 3` + +4. Do *not* run any `-M` modes, just running `-S` modes is better for CI + fuzzing. `-M` enables old queue handling etc. which is good for a fuzzing + campaign but not good for short CI runs. + +How this can look like can, e.g., be seen at AFL++'s setup in Google's +[oss-fuzz](https://github.com/google/oss-fuzz/blob/master/infra/base-images/base-builder/compile_afl) +and +[clusterfuzz](https://github.com/google/clusterfuzz/blob/master/src/clusterfuzz/_internal/bot/fuzzers/afl/launcher.py). + +## The End + +Check out the [FAQ](FAQ.md) if it maybe answers your question (that you might +not even have known you had ;-) ). + +This is basically all you need to know to professionally run fuzzing campaigns. +If you want to know more, the tons of texts in [docs/](./) will have you +covered. + +Note that there are also a lot of tools out there that help fuzzing with AFL++ +(some might be deprecated or unsupported), see +[third_party_tools.md](third_party_tools.md). \ No newline at end of file diff --git a/docs/important_changes.md b/docs/important_changes.md index 0c5c2243..877dfab2 100644 --- a/docs/important_changes.md +++ b/docs/important_changes.md @@ -36,7 +36,7 @@ behaviours and defaults: shared libraries, etc. Additionally QEMU 5.1 supports more CPU targets so this is really worth it. * When instrumenting targets, afl-cc will not supersede optimizations anymore - if any were given. This allows to fuzz targets build regularly like those + if any were given. This allows to fuzz targets build regularly like those for debug or release versions. * afl-fuzz: * if neither -M or -S is specified, `-S default` is assumed, so more @@ -47,7 +47,7 @@ behaviours and defaults: * -m none is now default, set memory limits (in MB) with e.g. -m 250 * deterministic fuzzing is now disabled by default (unless using -M) and can be enabled with -D - * a caching of testcases can now be performed and can be modified by + * a caching of test cases can now be performed and can be modified by editing config.h for TESTCASE_CACHE or by specifying the env variable `AFL_TESTCACHE_SIZE` (in MB). Good values are between 50-500 (default: 50). * -M mains do not perform trimming diff --git a/docs/interpreting_output.md b/docs/interpreting_output.md deleted file mode 100644 index 4bd705f2..00000000 --- a/docs/interpreting_output.md +++ /dev/null @@ -1,71 +0,0 @@ -# Interpreting output - -See the [status_screen.md](status_screen.md) file for information on -how to interpret the displayed stats and monitor the health of the process. Be -sure to consult this file especially if any UI elements are highlighted in red. - -The fuzzing process will continue until you press Ctrl-C. At a minimum, you want -to allow the fuzzer to complete one queue cycle, which may take anywhere from a -couple of hours to a week or so. - -There are three subdirectories created within the output directory and updated -in real-time: - - - queue/ - test cases for every distinctive execution path, plus all the - starting files given by the user. This is the synthesized corpus - mentioned in section 2. - - Before using this corpus for any other purposes, you can shrink - it to a smaller size using the afl-cmin tool. The tool will find - a smaller subset of files offering equivalent edge coverage. - - - crashes/ - unique test cases that cause the tested program to receive a - fatal signal (e.g., SIGSEGV, SIGILL, SIGABRT). The entries are - grouped by the received signal. - - - hangs/ - unique test cases that cause the tested program to time out. The - default time limit before something is classified as a hang is - the larger of 1 second and the value of the -t parameter. - The value can be fine-tuned by setting AFL_HANG_TMOUT, but this - is rarely necessary. - -Crashes and hangs are considered "unique" if the associated execution paths -involve any state transitions not seen in previously-recorded faults. If a -single bug can be reached in multiple ways, there will be some count inflation -early in the process, but this should quickly taper off. - -The file names for crashes and hangs are correlated with the parent, non-faulting -queue entries. This should help with debugging. - -When you can't reproduce a crash found by afl-fuzz, the most likely cause is -that you are not setting the same memory limit as used by the tool. Try: - -```shell -LIMIT_MB=50 -( ulimit -Sv $[LIMIT_MB << 10]; /path/to/tested_binary ... ) -``` - -Change LIMIT_MB to match the -m parameter passed to afl-fuzz. On OpenBSD, -also change -Sv to -Sd. - -Any existing output directory can be also used to resume aborted jobs; try: - -```shell -./afl-fuzz -i- -o existing_output_dir [...etc...] -``` - -If you have gnuplot installed, you can also generate some pretty graphs for any -active fuzzing task using afl-plot. For an example of how this looks like, -see [https://lcamtuf.coredump.cx/afl/plot/](https://lcamtuf.coredump.cx/afl/plot/). - -You can also manually build and install afl-plot-ui, which is a helper utility -for showing the graphs generated by afl-plot in a graphical window using GTK. -You can build and install it as follows - -```shell -sudo apt install libgtk-3-0 libgtk-3-dev pkg-config -cd utils/plot_ui -make -cd ../../ -sudo make install -``` diff --git a/docs/known_limitations.md b/docs/known_limitations.md deleted file mode 100644 index a68c0a85..00000000 --- a/docs/known_limitations.md +++ /dev/null @@ -1,36 +0,0 @@ -# Known limitations & areas for improvement - -Here are some of the most important caveats for AFL: - - - AFL++ detects faults by checking for the first spawned process dying due to - a signal (SIGSEGV, SIGABRT, etc). Programs that install custom handlers for - these signals may need to have the relevant code commented out. In the same - vein, faults in child processes spawned by the fuzzed target may evade - detection unless you manually add some code to catch that. - - - As with any other brute-force tool, the fuzzer offers limited coverage if - encryption, checksums, cryptographic signatures, or compression are used to - wholly wrap the actual data format to be tested. - - To work around this, you can comment out the relevant checks (see - utils/libpng_no_checksum/ for inspiration); if this is not possible, - you can also write a postprocessor, one of the hooks of custom mutators. - See [custom_mutators.md](custom_mutators.md) on how to use - `AFL_CUSTOM_MUTATOR_LIBRARY` - - - There are some unfortunate trade-offs with ASAN and 64-bit binaries. This - isn't due to any specific fault of afl-fuzz. - - - There is no direct support for fuzzing network services, background - daemons, or interactive apps that require UI interaction to work. You may - need to make simple code changes to make them behave in a more traditional - way. Preeny may offer a relatively simple option, too - see: - [https://github.com/zardus/preeny](https://github.com/zardus/preeny) - - Some useful tips for modifying network-based services can be also found at: - [https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop](https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop) - - - Occasionally, sentient machines rise against their creators. If this - happens to you, please consult [https://lcamtuf.coredump.cx/prep/](https://lcamtuf.coredump.cx/prep/). - -Beyond this, see [INSTALL.md](INSTALL.md) for platform-specific tips. diff --git a/docs/parallel_fuzzing.md b/docs/parallel_fuzzing.md deleted file mode 100644 index d24f2837..00000000 --- a/docs/parallel_fuzzing.md +++ /dev/null @@ -1,258 +0,0 @@ -# Tips for parallel fuzzing - -This document talks about synchronizing afl-fuzz jobs on a single machine -or across a fleet of systems. See README.md for the general instruction manual. - -Note that this document is rather outdated. please refer to the main document -section on multiple core usage [fuzzing_expert.md#Using multiple cores](fuzzing_expert.md#b-using-multiple-cores) -for up to date strategies! - -## 1) Introduction - -Every copy of afl-fuzz will take up one CPU core. This means that on an -n-core system, you can almost always run around n concurrent fuzzing jobs with -virtually no performance hit (you can use the afl-gotcpu tool to make sure). - -In fact, if you rely on just a single job on a multi-core system, you will -be underutilizing the hardware. So, parallelization is always the right way to -go. - -When targeting multiple unrelated binaries or using the tool in -"non-instrumented" (-n) mode, it is perfectly fine to just start up several -fully separate instances of afl-fuzz. The picture gets more complicated when -you want to have multiple fuzzers hammering a common target: if a hard-to-hit -but interesting test case is synthesized by one fuzzer, the remaining instances -will not be able to use that input to guide their work. - -To help with this problem, afl-fuzz offers a simple way to synchronize test -cases on the fly. - -It is a good idea to use different power schedules if you run several instances -in parallel (`-p` option). - -Alternatively running other AFL spinoffs in parallel can be of value, -e.g. Angora (https://github.com/AngoraFuzzer/Angora/) - -## 2) Single-system parallelization - -If you wish to parallelize a single job across multiple cores on a local -system, simply create a new, empty output directory ("sync dir") that will be -shared by all the instances of afl-fuzz; and then come up with a naming scheme -for every instance - say, "fuzzer01", "fuzzer02", etc. - -Run the first one ("main node", -M) like this: - -``` -./afl-fuzz -i testcase_dir -o sync_dir -M fuzzer01 [...other stuff...] -``` - -...and then, start up secondary (-S) instances like this: - -``` -./afl-fuzz -i testcase_dir -o sync_dir -S fuzzer02 [...other stuff...] -./afl-fuzz -i testcase_dir -o sync_dir -S fuzzer03 [...other stuff...] -``` - -Each fuzzer will keep its state in a separate subdirectory, like so: - - /path/to/sync_dir/fuzzer01/ - -Each instance will also periodically rescan the top-level sync directory -for any test cases found by other fuzzers - and will incorporate them into -its own fuzzing when they are deemed interesting enough. -For performance reasons only -M main node syncs the queue with everyone, the --S secondary nodes will only sync from the main node. - -The difference between the -M and -S modes is that the main instance will -still perform deterministic checks; while the secondary instances will -proceed straight to random tweaks. - -Note that you must always have one -M main instance! -Running multiple -M instances is wasteful! - -You can also monitor the progress of your jobs from the command line with the -provided afl-whatsup tool. When the instances are no longer finding new paths, -it's probably time to stop. - -WARNING: Exercise caution when explicitly specifying the -f option. Each fuzzer -must use a separate temporary file; otherwise, things will go south. One safe -example may be: - -``` -./afl-fuzz [...] -S fuzzer10 -f file10.txt ./fuzzed/binary @@ -./afl-fuzz [...] -S fuzzer11 -f file11.txt ./fuzzed/binary @@ -./afl-fuzz [...] -S fuzzer12 -f file12.txt ./fuzzed/binary @@ -``` - -This is not a concern if you use @@ without -f and let afl-fuzz come up with the -file name. - -## 3) Multiple -M mains - - -There is support for parallelizing the deterministic checks. -This is only needed where - - 1. many new paths are found fast over a long time and it looks unlikely that - main node will ever catch up, and - 2. deterministic fuzzing is actively helping path discovery (you can see this - in the main node for the first for lines in the "fuzzing strategy yields" - section. If the ration `found/attemps` is high, then it is effective. It - most commonly isn't.) - -Only if both are true it is beneficial to have more than one main. -You can leverage this by creating -M instances like so: - -``` -./afl-fuzz -i testcase_dir -o sync_dir -M mainA:1/3 [...] -./afl-fuzz -i testcase_dir -o sync_dir -M mainB:2/3 [...] -./afl-fuzz -i testcase_dir -o sync_dir -M mainC:3/3 [...] -``` - -... where the first value after ':' is the sequential ID of a particular main -instance (starting at 1), and the second value is the total number of fuzzers to -distribute the deterministic fuzzing across. Note that if you boot up fewer -fuzzers than indicated by the second number passed to -M, you may end up with -poor coverage. - -## 4) Syncing with non-AFL fuzzers or independant instances - -A -M main node can be told with the `-F other_fuzzer_queue_directory` option -to sync results from other fuzzers, e.g. libfuzzer or honggfuzz. - -Only the specified directory will by synced into afl, not subdirectories. -The specified directory does not need to exist yet at the start of afl. - -The `-F` option can be passed to the main node several times. - -## 5) Multi-system parallelization - -The basic operating principle for multi-system parallelization is similar to -the mechanism explained in section 2. The key difference is that you need to -write a simple script that performs two actions: - - - Uses SSH with authorized_keys to connect to every machine and retrieve - a tar archive of the /path/to/sync_dir/<main_node(s)> directory local to - the machine. - It is best to use a naming scheme that includes host name and it's being - a main node (e.g. main1, main2) in the fuzzer ID, so that you can do - something like: - - ```sh - for host in `cat HOSTLIST`; do - ssh user@$host "tar -czf - sync/$host_main*/" > $host.tgz - done - ``` - - - Distributes and unpacks these files on all the remaining machines, e.g.: - - ```sh - for srchost in `cat HOSTLIST`; do - for dsthost in `cat HOSTLIST`; do - test "$srchost" = "$dsthost" && continue - ssh user@$srchost 'tar -kxzf -' < $dsthost.tgz - done - done - ``` - -There is an example of such a script in utils/distributed_fuzzing/. - -There are other (older) more featured, experimental tools: - * https://github.com/richo/roving - * https://github.com/MartijnB/disfuzz-afl - -However these do not support syncing just main nodes (yet). - -When developing custom test case sync code, there are several optimizations -to keep in mind: - - - The synchronization does not have to happen very often; running the - task every 60 minutes or even less often at later fuzzing stages is - fine - - - There is no need to synchronize crashes/ or hangs/; you only need to - copy over queue/* (and ideally, also fuzzer_stats). - - - It is not necessary (and not advisable!) to overwrite existing files; - the -k option in tar is a good way to avoid that. - - - There is no need to fetch directories for fuzzers that are not running - locally on a particular machine, and were simply copied over onto that - system during earlier runs. - - - For large fleets, you will want to consolidate tarballs for each host, - as this will let you use n SSH connections for sync, rather than n*(n-1). - - You may also want to implement staged synchronization. For example, you - could have 10 groups of systems, with group 1 pushing test cases only - to group 2; group 2 pushing them only to group 3; and so on, with group - eventually 10 feeding back to group 1. - - This arrangement would allow test interesting cases to propagate across - the fleet without having to copy every fuzzer queue to every single host. - - - You do not want a "main" instance of afl-fuzz on every system; you should - run them all with -S, and just designate a single process somewhere within - the fleet to run with -M. - - - Syncing is only necessary for the main nodes on a system. It is possible - to run main-less with only secondaries. However then you need to find out - which secondary took over the temporary role to be the main node. Look for - the `is_main_node` file in the fuzzer directories, eg. `sync-dir/hostname-*/is_main_node` - -It is *not* advisable to skip the synchronization script and run the fuzzers -directly on a network filesystem; unexpected latency and unkillable processes -in I/O wait state can mess things up. - -## 6) Remote monitoring and data collection - -You can use screen, nohup, tmux, or something equivalent to run remote -instances of afl-fuzz. If you redirect the program's output to a file, it will -automatically switch from a fancy UI to more limited status reports. There is -also basic machine-readable information which is always written to the -fuzzer_stats file in the output directory. Locally, that information can be -interpreted with afl-whatsup. - -In principle, you can use the status screen of the main (-M) instance to -monitor the overall fuzzing progress and decide when to stop. In this -mode, the most important signal is just that no new paths are being found -for a longer while. If you do not have a main instance, just pick any -single secondary instance to watch and go by that. - -You can also rely on that instance's output directory to collect the -synthesized corpus that covers all the noteworthy paths discovered anywhere -within the fleet. Secondary (-S) instances do not require any special -monitoring, other than just making sure that they are up. - -Keep in mind that crashing inputs are *not* automatically propagated to the -main instance, so you may still want to monitor for crashes fleet-wide -from within your synchronization or health checking scripts (see afl-whatsup). - -## 7) Asymmetric setups - -It is perhaps worth noting that all of the following is permitted: - - - Running afl-fuzz with conjunction with other guided tools that can extend - coverage (e.g., via concolic execution). Third-party tools simply need to - follow the protocol described above for pulling new test cases from - out_dir/<fuzzer_id>/queue/* and writing their own finds to sequentially - numbered id:nnnnnn files in out_dir/<ext_tool_id>/queue/*. - - - Running some of the synchronized fuzzers with different (but related) - target binaries. For example, simultaneously stress-testing several - different JPEG parsers (say, IJG jpeg and libjpeg-turbo) while sharing - the discovered test cases can have synergistic effects and improve the - overall coverage. - - (In this case, running one -M instance per target is necessary.) - - - Having some of the fuzzers invoke the binary in different ways. - For example, 'djpeg' supports several DCT modes, configurable with - a command-line flag, while 'dwebp' supports incremental and one-shot - decoding. In some scenarios, going after multiple distinct modes and then - pooling test cases will improve coverage. - - - Much less convincingly, running the synchronized fuzzers with different - starting test cases (e.g., progressive and standard JPEG) or dictionaries. - The synchronization mechanism ensures that the test sets will get fairly - homogeneous over time, but it introduces some initial variability. diff --git a/docs/perf_tips.md b/docs/perf_tips.md deleted file mode 100644 index 1e8fd4d0..00000000 --- a/docs/perf_tips.md +++ /dev/null @@ -1,209 +0,0 @@ -## Tips for performance optimization - - This file provides tips for troubleshooting slow or wasteful fuzzing jobs. - See README.md for the general instruction manual. - -## 1. Keep your test cases small - -This is probably the single most important step to take! Large test cases do -not merely take more time and memory to be parsed by the tested binary, but -also make the fuzzing process dramatically less efficient in several other -ways. - -To illustrate, let's say that you're randomly flipping bits in a file, one bit -at a time. Let's assume that if you flip bit #47, you will hit a security bug; -flipping any other bit just results in an invalid document. - -Now, if your starting test case is 100 bytes long, you will have a 71% chance of -triggering the bug within the first 1,000 execs - not bad! But if the test case -is 1 kB long, the probability that we will randomly hit the right pattern in -the same timeframe goes down to 11%. And if it has 10 kB of non-essential -cruft, the odds plunge to 1%. - -On top of that, with larger inputs, the binary may be now running 5-10x times -slower than before - so the overall drop in fuzzing efficiency may be easily -as high as 500x or so. - -In practice, this means that you shouldn't fuzz image parsers with your -vacation photos. Generate a tiny 16x16 picture instead, and run it through -`jpegtran` or `pngcrunch` for good measure. The same goes for most other types -of documents. - -There's plenty of small starting test cases in ../testcases/ - try them out -or submit new ones! - -If you want to start with a larger, third-party corpus, run `afl-cmin` with an -aggressive timeout on that data set first. - -## 2. Use a simpler target - -Consider using a simpler target binary in your fuzzing work. For example, for -image formats, bundled utilities such as `djpeg`, `readpng`, or `gifhisto` are -considerably (10-20x) faster than the convert tool from ImageMagick - all while exercising roughly the same library-level image parsing code. - -Even if you don't have a lightweight harness for a particular target, remember -that you can always use another, related library to generate a corpus that will -be then manually fed to a more resource-hungry program later on. - -Also note that reading the fuzzing input via stdin is faster than reading from -a file. - -## 3. Use LLVM persistent instrumentation - -The LLVM mode offers a "persistent", in-process fuzzing mode that can -work well for certain types of self-contained libraries, and for fast targets, -can offer performance gains up to 5-10x; and a "deferred fork server" mode -that can offer huge benefits for programs with high startup overhead. Both -modes require you to edit the source code of the fuzzed program, but the -changes often amount to just strategically placing a single line or two. - -If there are important data comparisons performed (e.g. `strcmp(ptr, MAGIC_HDR)`) -then using laf-intel (see instrumentation/README.laf-intel.md) will help `afl-fuzz` a lot -to get to the important parts in the code. - -If you are only interested in specific parts of the code being fuzzed, you can -instrument_files the files that are actually relevant. This improves the speed and -accuracy of afl. See instrumentation/README.instrument_list.md - -## 4. Profile and optimize the binary - -Check for any parameters or settings that obviously improve performance. For -example, the djpeg utility that comes with IJG jpeg and libjpeg-turbo can be -called with: - -```bash - -dct fast -nosmooth -onepass -dither none -scale 1/4 -``` - -...and that will speed things up. There is a corresponding drop in the quality -of decoded images, but it's probably not something you care about. - -In some programs, it is possible to disable output altogether, or at least use -an output format that is computationally inexpensive. For example, with image -transcoding tools, converting to a BMP file will be a lot faster than to PNG. - -With some laid-back parsers, enabling "strict" mode (i.e., bailing out after -first error) may result in smaller files and improved run time without -sacrificing coverage; for example, for sqlite, you may want to specify -bail. - -If the program is still too slow, you can use `strace -tt` or an equivalent -profiling tool to see if the targeted binary is doing anything silly. -Sometimes, you can speed things up simply by specifying `/dev/null` as the -config file, or disabling some compile-time features that aren't really needed -for the job (try `./configure --help`). One of the notoriously resource-consuming -things would be calling other utilities via `exec*()`, `popen()`, `system()`, or -equivalent calls; for example, tar can invoke external decompression tools -when it decides that the input file is a compressed archive. - -Some programs may also intentionally call `sleep()`, `usleep()`, or `nanosleep()`; -vim is a good example of that. Other programs may attempt `fsync()` and so on. -There are third-party libraries that make it easy to get rid of such code, -e.g.: - - https://launchpad.net/libeatmydata - -In programs that are slow due to unavoidable initialization overhead, you may -want to try the LLVM deferred forkserver mode (see README.llvm.md), -which can give you speed gains up to 10x, as mentioned above. - -Last but not least, if you are using ASAN and the performance is unacceptable, -consider turning it off for now, and manually examining the generated corpus -with an ASAN-enabled binary later on. - -## 5. Instrument just what you need - -Instrument just the libraries you actually want to stress-test right now, one -at a time. Let the program use system-wide, non-instrumented libraries for -any functionality you don't actually want to fuzz. For example, in most -cases, it doesn't make to instrument `libgmp` just because you're testing a -crypto app that relies on it for bignum math. - -Beware of programs that come with oddball third-party libraries bundled with -their source code (Spidermonkey is a good example of this). Check `./configure` -options to use non-instrumented system-wide copies instead. - -## 6. Parallelize your fuzzers - -The fuzzer is designed to need ~1 core per job. This means that on a, say, -4-core system, you can easily run four parallel fuzzing jobs with relatively -little performance hit. For tips on how to do that, see parallel_fuzzing.md. - -The `afl-gotcpu` utility can help you understand if you still have idle CPU -capacity on your system. (It won't tell you about memory bandwidth, cache -misses, or similar factors, but they are less likely to be a concern.) - -## 7. Keep memory use and timeouts in check - -Consider setting low values for `-m` and `-t`. - -For programs that are nominally very fast, but get sluggish for some inputs, -you can also try setting `-t` values that are more punishing than what `afl-fuzz` -dares to use on its own. On fast and idle machines, going down to `-t 5` may be -a viable plan. - -The `-m` parameter is worth looking at, too. Some programs can end up spending -a fair amount of time allocating and initializing megabytes of memory when -presented with pathological inputs. Low `-m` values can make them give up sooner -and not waste CPU time. - -## 8. Check OS configuration - -There are several OS-level factors that may affect fuzzing speed: - - - If you have no risk of power loss then run your fuzzing on a tmpfs - partition. This increases the performance noticably. - Alternatively you can use `AFL_TMPDIR` to point to a tmpfs location to - just write the input file to a tmpfs. - - High system load. Use idle machines where possible. Kill any non-essential - CPU hogs (idle browser windows, media players, complex screensavers, etc). - - Network filesystems, either used for fuzzer input / output, or accessed by - the fuzzed binary to read configuration files (pay special attention to the - home directory - many programs search it for dot-files). - - Disable all the spectre, meltdown etc. security countermeasures in the - kernel if your machine is properly separated: - -``` -ibpb=off ibrs=off kpti=off l1tf=off mds=off mitigations=off -no_stf_barrier noibpb noibrs nopcid nopti nospec_store_bypass_disable -nospectre_v1 nospectre_v2 pcid=off pti=off spec_store_bypass_disable=off -spectre_v2=off stf_barrier=off -``` - In most Linux distributions you can put this into a `/etc/default/grub` - variable. - You can use `sudo afl-persistent-config` to set these options for you. - -The following list of changes are made when executing `afl-system-config`: - - - On-demand CPU scaling. The Linux `ondemand` governor performs its analysis - on a particular schedule and is known to underestimate the needs of - short-lived processes spawned by `afl-fuzz` (or any other fuzzer). On Linux, - this can be fixed with: - -``` bash - cd /sys/devices/system/cpu - echo performance | tee cpu*/cpufreq/scaling_governor -``` - - On other systems, the impact of CPU scaling will be different; when fuzzing, - use OS-specific tools to find out if all cores are running at full speed. - - Transparent huge pages. Some allocators, such as `jemalloc`, can incur a - heavy fuzzing penalty when transparent huge pages (THP) are enabled in the - kernel. You can disable this via: - -```bash - echo never > /sys/kernel/mm/transparent_hugepage/enabled -``` - - - Suboptimal scheduling strategies. The significance of this will vary from - one target to another, but on Linux, you may want to make sure that the - following options are set: - -```bash - echo 1 >/proc/sys/kernel/sched_child_runs_first - echo 1 >/proc/sys/kernel/sched_autogroup_enabled -``` - - Setting a different scheduling policy for the fuzzer process - say - `SCHED_RR` - can usually speed things up, too, but needs to be done with - care. - diff --git a/docs/sister_projects.md b/docs/sister_projects.md deleted file mode 100644 index 613bc778..00000000 --- a/docs/sister_projects.md +++ /dev/null @@ -1,319 +0,0 @@ -# Sister projects - -This doc lists some of the projects that are inspired by, derived from, -designed for, or meant to integrate with AFL. See README.md for the general -instruction manual. - -!!! -!!! This list is outdated and needs an update, missing: e.g. Angora, FairFuzz -!!! - -## Support for other languages / environments: - -### Python AFL (Jakub Wilk) - -Allows fuzz-testing of Python programs. Uses custom instrumentation and its -own forkserver. - -https://jwilk.net/software/python-afl - -### Go-fuzz (Dmitry Vyukov) - -AFL-inspired guided fuzzing approach for Go targets: - -https://github.com/dvyukov/go-fuzz - -### afl.rs (Keegan McAllister) - -Allows Rust features to be easily fuzzed with AFL (using the LLVM mode). - -https://github.com/kmcallister/afl.rs - -### OCaml support (KC Sivaramakrishnan) - -Adds AFL-compatible instrumentation to OCaml programs. - -https://github.com/ocamllabs/opam-repo-dev/pull/23 -https://canopy.mirage.io/Posts/Fuzzing - -### AFL for GCJ Java and other GCC frontends (-) - -GCC Java programs are actually supported out of the box - simply rename -afl-gcc to afl-gcj. Unfortunately, by default, unhandled exceptions in GCJ do -not result in abort() being called, so you will need to manually add a -top-level exception handler that exits with SIGABRT or something equivalent. - -Other GCC-supported languages should be fairly easy to get working, but may -face similar problems. See https://gcc.gnu.org/frontends.html for a list of -options. - -## AFL-style in-process fuzzer for LLVM (Kostya Serebryany) - -Provides an evolutionary instrumentation-guided fuzzing harness that allows -some programs to be fuzzed without the fork / execve overhead. (Similar -functionality is now available as the "persistent" feature described in -[the llvm_mode readme](../instrumentation/README.llvm.md)) - -https://llvm.org/docs/LibFuzzer.html - -## TriforceAFL (Tim Newsham and Jesse Hertz) - -Leverages QEMU full system emulation mode to allow AFL to target operating -systems and other alien worlds: - -https://www.nccgroup.trust/us/about-us/newsroom-and-events/blog/2016/june/project-triforce-run-afl-on-everything/ - -## WinAFL (Ivan Fratric) - -As the name implies, allows you to fuzz Windows binaries (using DynamoRio). - -https://github.com/ivanfratric/winafl - -Another Windows alternative may be: - -https://github.com/carlosgprado/BrundleFuzz/ - -## Network fuzzing - -### Preeny (Yan Shoshitaishvili) - -Provides a fairly simple way to convince dynamically linked network-centric -programs to read from a file or not fork. Not AFL-specific, but described as -useful by many users. Some assembly required. - -https://github.com/zardus/preeny - -## Distributed fuzzing and related automation - -### roving (Richo Healey) - -A client-server architecture for effortlessly orchestrating AFL runs across -a fleet of machines. You don't want to use this on systems that face the -Internet or live in other untrusted environments. - -https://github.com/richo/roving - -### Distfuzz-AFL (Martijn Bogaard) - -Simplifies the management of afl-fuzz instances on remote machines. The -author notes that the current implementation isn't secure and should not -be exposed on the Internet. - -https://github.com/MartijnB/disfuzz-afl - -### AFLDFF (quantumvm) - -A nice GUI for managing AFL jobs. - -https://github.com/quantumvm/AFLDFF - -### afl-launch (Ben Nagy) - -Batch AFL launcher utility with a simple CLI. - -https://github.com/bnagy/afl-launch - -### AFL Utils (rc0r) - -Simplifies the triage of discovered crashes, start parallel instances, etc. - -https://github.com/rc0r/afl-utils - -### AFL crash analyzer (floyd) - -Another crash triage tool: - -https://github.com/floyd-fuh/afl-crash-analyzer - -### afl-extras (fekir) - -Collect data, parallel afl-tmin, startup scripts. - -https://github.com/fekir/afl-extras - -### afl-fuzzing-scripts (Tobias Ospelt) - -Simplifies starting up multiple parallel AFL jobs. - -https://github.com/floyd-fuh/afl-fuzzing-scripts/ - -### afl-sid (Jacek Wielemborek) - -Allows users to more conveniently build and deploy AFL via Docker. - -https://github.com/d33tah/afl-sid - -Another Docker-related project: - -https://github.com/ozzyjohnson/docker-afl - -### afl-monitor (Paul S. Ziegler) - -Provides more detailed and versatile statistics about your running AFL jobs. - -https://github.com/reflare/afl-monitor - -### FEXM (Security in Telecommunications) - -Fully automated fuzzing framework, based on AFL - -https://github.com/fgsect/fexm - -## Crash triage, coverage analysis, and other companion tools: - -### afl-crash-analyzer (Tobias Ospelt) - -Makes it easier to navigate and annotate crashing test cases. - -https://github.com/floyd-fuh/afl-crash-analyzer/ - -### Crashwalk (Ben Nagy) - -AFL-aware tool to annotate and sort through crashing test cases. - -https://github.com/bnagy/crashwalk - -### afl-cov (Michael Rash) - -Produces human-readable coverage data based on the output queue of afl-fuzz. - -https://github.com/mrash/afl-cov - -### afl-sancov (Bhargava Shastry) - -Similar to afl-cov, but uses clang sanitizer instrumentation. - -https://github.com/bshastry/afl-sancov - -### RecidiVM (Jakub Wilk) - -Makes it easy to estimate memory usage limits when fuzzing with ASAN or MSAN. - -https://jwilk.net/software/recidivm - -### aflize (Jacek Wielemborek) - -Automatically build AFL-enabled versions of Debian packages. - -https://github.com/d33tah/aflize - -### afl-ddmin-mod (Markus Teufelberger) - -A variant of afl-tmin that uses a more sophisticated (but slower) -minimization algorithm. - -https://github.com/MarkusTeufelberger/afl-ddmin-mod - -### afl-kit (Kuang-che Wu) - -Replacements for afl-cmin and afl-tmin with additional features, such -as the ability to filter crashes based on stderr patterns. - -https://github.com/kcwu/afl-kit - -## Narrow-purpose or experimental: - -### Cygwin support (Ali Rizvi-Santiago) - -Pretty self-explanatory. As per the author, this "mostly" ports AFL to -Windows. Field reports welcome! - -https://github.com/arizvisa/afl-cygwin - -### Pause and resume scripts (Ben Nagy) - -Simple automation to suspend and resume groups of fuzzing jobs. - -https://github.com/bnagy/afl-trivia - -### Static binary-only instrumentation (Aleksandar Nikolich) - -Allows black-box binaries to be instrumented statically (i.e., by modifying -the binary ahead of the time, rather than translating it on the run). Author -reports better performance compared to QEMU, but occasional translation -errors with stripped binaries. - -https://github.com/vanhauser-thc/afl-dyninst - -### AFL PIN (Parker Thompson) - -Early-stage Intel PIN instrumentation support (from before we settled on -faster-running QEMU). - -https://github.com/mothran/aflpin - -### AFL-style instrumentation in llvm (Kostya Serebryany) - -Allows AFL-equivalent instrumentation to be injected at compiler level. -This is currently not supported by AFL as-is, but may be useful in other -projects. - -https://code.google.com/p/address-sanitizer/wiki/AsanCoverage#Coverage_counters - -### AFL JS (Han Choongwoo) - -One-off optimizations to speed up the fuzzing of JavaScriptCore (now likely -superseded by LLVM deferred forkserver init - see README.llvm.md). - -https://github.com/tunz/afl-fuzz-js - -### AFL harness for fwknop (Michael Rash) - -An example of a fairly involved integration with AFL. - -https://github.com/mrash/fwknop/tree/master/test/afl - -### Building harnesses for DNS servers (Jonathan Foote, Ron Bowes) - -Two articles outlining the general principles and showing some example code. - -https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop -https://goo.gl/j9EgFf - -### Fuzzer shell for SQLite (Richard Hipp) - -A simple SQL shell designed specifically for fuzzing the underlying library. - -https://www.sqlite.org/src/artifact/9e7e273da2030371 - -### Support for Python mutation modules (Christian Holler) - -now integrated in AFL++, originally from here -https://github.com/choller/afl/blob/master/docs/mozilla/python_modules.txt - -### Support for selective instrumentation (Christian Holler) - -now integrated in AFL++, originally from here -https://github.com/choller/afl/blob/master/docs/mozilla/partial_instrumentation.txt - -### Syzkaller (Dmitry Vyukov) - -A similar guided approach as applied to fuzzing syscalls: - -https://github.com/google/syzkaller/wiki/Found-Bugs -https://github.com/dvyukov/linux/commit/33787098ffaaa83b8a7ccf519913ac5fd6125931 -https://events.linuxfoundation.org/sites/events/files/slides/AFL%20filesystem%20fuzzing%2C%20Vault%202016_0.pdf - - -### Kernel Snapshot Fuzzing using Unicornafl (Security in Telecommunications) - -https://github.com/fgsect/unicorefuzz - -### Android support (ele7enxxh) - -Based on a somewhat dated version of AFL: - -https://github.com/ele7enxxh/android-afl - -### CGI wrapper (floyd) - -Facilitates the testing of CGI scripts. - -https://github.com/floyd-fuh/afl-cgi-wrapper - -### Fuzzing difficulty estimation (Marcel Boehme) - -A fork of AFL that tries to quantify the likelihood of finding additional -paths or crashes at any point in a fuzzing job. - -https://github.com/mboehme/pythia diff --git a/docs/status_screen.md b/docs/status_screen.md deleted file mode 100644 index b1cb9696..00000000 --- a/docs/status_screen.md +++ /dev/null @@ -1,444 +0,0 @@ -# Understanding the status screen - -This document provides an overview of the status screen - plus tips for -troubleshooting any warnings and red text shown in the UI. See README.md for -the general instruction manual. - -## A note about colors - -The status screen and error messages use colors to keep things readable and -attract your attention to the most important details. For example, red almost -always means "consult this doc" :-) - -Unfortunately, the UI will render correctly only if your terminal is using -traditional un*x palette (white text on black background) or something close -to that. - -If you are using inverse video, you may want to change your settings, say: - -- For GNOME Terminal, go to `Edit > Profile` preferences, select the "colors" tab, and from the list of built-in schemes, choose "white on black". -- For the MacOS X Terminal app, open a new window using the "Pro" scheme via the `Shell > New Window` menu (or make "Pro" your default). - -Alternatively, if you really like your current colors, you can edit config.h -to comment out USE_COLORS, then do `make clean all`. - -I'm not aware of any other simple way to make this work without causing -other side effects - sorry about that. - -With that out of the way, let's talk about what's actually on the screen... - -### The status bar - -``` -american fuzzy lop ++3.01a (default) [fast] {0} -``` - -The top line shows you which mode afl-fuzz is running in -(normal: "american fuzy lop", crash exploration mode: "peruvian rabbit mode") -and the version of AFL++. -Next to the version is the banner, which, if not set with -T by hand, will -either show the binary name being fuzzed, or the -M/-S main/secondary name for -parallel fuzzing. -Second to last is the power schedule mode being run (default: fast). -Finally, the last item is the CPU id. - -### Process timing - -``` - +----------------------------------------------------+ - | run time : 0 days, 8 hrs, 32 min, 43 sec | - | last new path : 0 days, 0 hrs, 6 min, 40 sec | - | last uniq crash : none seen yet | - | last uniq hang : 0 days, 1 hrs, 24 min, 32 sec | - +----------------------------------------------------+ -``` - -This section is fairly self-explanatory: it tells you how long the fuzzer has -been running and how much time has elapsed since its most recent finds. This is -broken down into "paths" (a shorthand for test cases that trigger new execution -patterns), crashes, and hangs. - -When it comes to timing: there is no hard rule, but most fuzzing jobs should be -expected to run for days or weeks; in fact, for a moderately complex project, the -first pass will probably take a day or so. Every now and then, some jobs -will be allowed to run for months. - -There's one important thing to watch out for: if the tool is not finding new -paths within several minutes of starting, you're probably not invoking the -target binary correctly and it never gets to parse the input files we're -throwing at it; another possible explanations are that the default memory limit -(`-m`) is too restrictive, and the program exits after failing to allocate a -buffer very early on; or that the input files are patently invalid and always -fail a basic header check. - -If there are no new paths showing up for a while, you will eventually see a big -red warning in this section, too :-) - -### Overall results - -``` - +-----------------------+ - | cycles done : 0 | - | total paths : 2095 | - | uniq crashes : 0 | - | uniq hangs : 19 | - +-----------------------+ -``` - -The first field in this section gives you the count of queue passes done so far - that is, the number of times the fuzzer went over all the interesting test -cases discovered so far, fuzzed them, and looped back to the very beginning. -Every fuzzing session should be allowed to complete at least one cycle; and -ideally, should run much longer than that. - -As noted earlier, the first pass can take a day or longer, so sit back and -relax. - -To help make the call on when to hit `Ctrl-C`, the cycle counter is color-coded. -It is shown in magenta during the first pass, progresses to yellow if new finds -are still being made in subsequent rounds, then blue when that ends - and -finally, turns green after the fuzzer hasn't been seeing any action for a -longer while. - -The remaining fields in this part of the screen should be pretty obvious: -there's the number of test cases ("paths") discovered so far, and the number of -unique faults. The test cases, crashes, and hangs can be explored in real-time -by browsing the output directory, as discussed in README.md. - -### Cycle progress - -``` - +-------------------------------------+ - | now processing : 1296 (61.86%) | - | paths timed out : 0 (0.00%) | - +-------------------------------------+ -``` - -This box tells you how far along the fuzzer is with the current queue cycle: it -shows the ID of the test case it is currently working on, plus the number of -inputs it decided to ditch because they were persistently timing out. - -The "*" suffix sometimes shown in the first line means that the currently -processed path is not "favored" (a property discussed later on). - -### Map coverage - -``` - +--------------------------------------+ - | map density : 10.15% / 29.07% | - | count coverage : 4.03 bits/tuple | - +--------------------------------------+ -``` - -The section provides some trivia about the coverage observed by the -instrumentation embedded in the target binary. - -The first line in the box tells you how many branch tuples we have already -hit, in proportion to how much the bitmap can hold. The number on the left -describes the current input; the one on the right is the value for the entire -input corpus. - -Be wary of extremes: - - - Absolute numbers below 200 or so suggest one of three things: that the - program is extremely simple; that it is not instrumented properly (e.g., - due to being linked against a non-instrumented copy of the target - library); or that it is bailing out prematurely on your input test cases. - The fuzzer will try to mark this in pink, just to make you aware. - - Percentages over 70% may very rarely happen with very complex programs - that make heavy use of template-generated code. - Because high bitmap density makes it harder for the fuzzer to reliably - discern new program states, I recommend recompiling the binary with - `AFL_INST_RATIO=10` or so and trying again (see env_variables.md). - The fuzzer will flag high percentages in red. Chances are, you will never - see that unless you're fuzzing extremely hairy software (say, v8, perl, - ffmpeg). - -The other line deals with the variability in tuple hit counts seen in the -binary. In essence, if every taken branch is always taken a fixed number of -times for all the inputs we have tried, this will read `1.00`. As we manage -to trigger other hit counts for every branch, the needle will start to move -toward `8.00` (every bit in the 8-bit map hit), but will probably never -reach that extreme. - -Together, the values can be useful for comparing the coverage of several -different fuzzing jobs that rely on the same instrumented binary. - -### Stage progress - -``` - +-------------------------------------+ - | now trying : interest 32/8 | - | stage execs : 3996/34.4k (11.62%) | - | total execs : 27.4M | - | exec speed : 891.7/sec | - +-------------------------------------+ -``` - -This part gives you an in-depth peek at what the fuzzer is actually doing right -now. It tells you about the current stage, which can be any of: - - - calibration - a pre-fuzzing stage where the execution path is examined - to detect anomalies, establish baseline execution speed, and so on. Executed - very briefly whenever a new find is being made. - - trim L/S - another pre-fuzzing stage where the test case is trimmed to the - shortest form that still produces the same execution path. The length (L) - and stepover (S) are chosen in general relationship to file size. - - bitflip L/S - deterministic bit flips. There are L bits toggled at any given - time, walking the input file with S-bit increments. The current L/S variants - are: `1/1`, `2/1`, `4/1`, `8/8`, `16/8`, `32/8`. - - arith L/8 - deterministic arithmetics. The fuzzer tries to subtract or add - small integers to 8-, 16-, and 32-bit values. The stepover is always 8 bits. - - interest L/8 - deterministic value overwrite. The fuzzer has a list of known - "interesting" 8-, 16-, and 32-bit values to try. The stepover is 8 bits. - - extras - deterministic injection of dictionary terms. This can be shown as - "user" or "auto", depending on whether the fuzzer is using a user-supplied - dictionary (`-x`) or an auto-created one. You will also see "over" or "insert", - depending on whether the dictionary words overwrite existing data or are - inserted by offsetting the remaining data to accommodate their length. - - havoc - a sort-of-fixed-length cycle with stacked random tweaks. The - operations attempted during this stage include bit flips, overwrites with - random and "interesting" integers, block deletion, block duplication, plus - assorted dictionary-related operations (if a dictionary is supplied in the - first place). - - splice - a last-resort strategy that kicks in after the first full queue - cycle with no new paths. It is equivalent to 'havoc', except that it first - splices together two random inputs from the queue at some arbitrarily - selected midpoint. - - sync - a stage used only when `-M` or `-S` is set (see parallel_fuzzing.md). - No real fuzzing is involved, but the tool scans the output from other - fuzzers and imports test cases as necessary. The first time this is done, - it may take several minutes or so. - -The remaining fields should be fairly self-evident: there's the exec count -progress indicator for the current stage, a global exec counter, and a -benchmark for the current program execution speed. This may fluctuate from -one test case to another, but the benchmark should be ideally over 500 execs/sec -most of the time - and if it stays below 100, the job will probably take very -long. - -The fuzzer will explicitly warn you about slow targets, too. If this happens, -see the [perf_tips.md](perf_tips.md) file included with the fuzzer for ideas on how to speed -things up. - -### Findings in depth - -``` - +--------------------------------------+ - | favored paths : 879 (41.96%) | - | new edges on : 423 (20.19%) | - | total crashes : 0 (0 unique) | - | total tmouts : 24 (19 unique) | - +--------------------------------------+ -``` - -This gives you several metrics that are of interest mostly to complete nerds. -The section includes the number of paths that the fuzzer likes the most based -on a minimization algorithm baked into the code (these will get considerably -more air time), and the number of test cases that actually resulted in better -edge coverage (versus just pushing the branch hit counters up). There are also -additional, more detailed counters for crashes and timeouts. - -Note that the timeout counter is somewhat different from the hang counter; this -one includes all test cases that exceeded the timeout, even if they did not -exceed it by a margin sufficient to be classified as hangs. - -### Fuzzing strategy yields - -``` - +-----------------------------------------------------+ - | bit flips : 57/289k, 18/289k, 18/288k | - | byte flips : 0/36.2k, 4/35.7k, 7/34.6k | - | arithmetics : 53/2.54M, 0/537k, 0/55.2k | - | known ints : 8/322k, 12/1.32M, 10/1.70M | - | dictionary : 9/52k, 1/53k, 1/24k | - |havoc/splice : 1903/20.0M, 0/0 | - |py/custom/rq : unused, 53/2.54M, unused | - | trim/eff : 20.31%/9201, 17.05% | - +-----------------------------------------------------+ -``` - -This is just another nerd-targeted section keeping track of how many paths we -have netted, in proportion to the number of execs attempted, for each of the -fuzzing strategies discussed earlier on. This serves to convincingly validate -assumptions about the usefulness of the various approaches taken by afl-fuzz. - -The trim strategy stats in this section are a bit different than the rest. -The first number in this line shows the ratio of bytes removed from the input -files; the second one corresponds to the number of execs needed to achieve this -goal. Finally, the third number shows the proportion of bytes that, although -not possible to remove, were deemed to have no effect and were excluded from -some of the more expensive deterministic fuzzing steps. - -Note that when deterministic mutation mode is off (which is the default -because it is not very efficient) the first five lines display -"disabled (default, enable with -D)". - -Only what is activated will have counter shown. - -### Path geometry - -``` - +---------------------+ - | levels : 5 | - | pending : 1570 | - | pend fav : 583 | - | own finds : 0 | - | imported : 0 | - | stability : 100.00% | - +---------------------+ -``` - -The first field in this section tracks the path depth reached through the -guided fuzzing process. In essence: the initial test cases supplied by the -user are considered "level 1". The test cases that can be derived from that -through traditional fuzzing are considered "level 2"; the ones derived by -using these as inputs to subsequent fuzzing rounds are "level 3"; and so forth. -The maximum depth is therefore a rough proxy for how much value you're getting -out of the instrumentation-guided approach taken by afl-fuzz. - -The next field shows you the number of inputs that have not gone through any -fuzzing yet. The same stat is also given for "favored" entries that the fuzzer -really wants to get to in this queue cycle (the non-favored entries may have to -wait a couple of cycles to get their chance). - -Next, we have the number of new paths found during this fuzzing section and -imported from other fuzzer instances when doing parallelized fuzzing; and the -extent to which identical inputs appear to sometimes produce variable behavior -in the tested binary. - -That last bit is actually fairly interesting: it measures the consistency of -observed traces. If a program always behaves the same for the same input data, -it will earn a score of 100%. When the value is lower but still shown in purple, -the fuzzing process is unlikely to be negatively affected. If it goes into red, -you may be in trouble, since AFL will have difficulty discerning between -meaningful and "phantom" effects of tweaking the input file. - -Now, most targets will just get a 100% score, but when you see lower figures, -there are several things to look at: - - - The use of uninitialized memory in conjunction with some intrinsic sources - of entropy in the tested binary. Harmless to AFL, but could be indicative - of a security bug. - - Attempts to manipulate persistent resources, such as left over temporary - files or shared memory objects. This is usually harmless, but you may want - to double-check to make sure the program isn't bailing out prematurely. - Running out of disk space, SHM handles, or other global resources can - trigger this, too. - - Hitting some functionality that is actually designed to behave randomly. - Generally harmless. For example, when fuzzing sqlite, an input like - `select random();` will trigger a variable execution path. - - Multiple threads executing at once in semi-random order. This is harmless - when the 'stability' metric stays over 90% or so, but can become an issue - if not. Here's what to try: - * Use afl-clang-fast from [instrumentation](../instrumentation/) - it uses a thread-local tracking - model that is less prone to concurrency issues, - * See if the target can be compiled or run without threads. Common - `./configure` options include `--without-threads`, `--disable-pthreads`, or - `--disable-openmp`. - * Replace pthreads with GNU Pth (https://www.gnu.org/software/pth/), which - allows you to use a deterministic scheduler. - - In persistent mode, minor drops in the "stability" metric can be normal, - because not all the code behaves identically when re-entered; but major - dips may signify that the code within `__AFL_LOOP()` is not behaving - correctly on subsequent iterations (e.g., due to incomplete clean-up or - reinitialization of the state) and that most of the fuzzing effort goes - to waste. - -The paths where variable behavior is detected are marked with a matching entry -in the `<out_dir>/queue/.state/variable_behavior/` directory, so you can look -them up easily. - -### CPU load - -``` - [cpu: 25%] -``` - -This tiny widget shows the apparent CPU utilization on the local system. It is -calculated by taking the number of processes in the "runnable" state, and then -comparing it to the number of logical cores on the system. - -If the value is shown in green, you are using fewer CPU cores than available on -your system and can probably parallelize to improve performance; for tips on -how to do that, see parallel_fuzzing.md. - -If the value is shown in red, your CPU is *possibly* oversubscribed, and -running additional fuzzers may not give you any benefits. - -Of course, this benchmark is very simplistic; it tells you how many processes -are ready to run, but not how resource-hungry they may be. It also doesn't -distinguish between physical cores, logical cores, and virtualized CPUs; the -performance characteristics of each of these will differ quite a bit. - -If you want a more accurate measurement, you can run the `afl-gotcpu` utility from the command line. - -### Addendum: status and plot files - -For unattended operation, some of the key status screen information can be also -found in a machine-readable format in the fuzzer_stats file in the output -directory. This includes: - - - `start_time` - unix time indicating the start time of afl-fuzz - - `last_update` - unix time corresponding to the last update of this file - - `run_time` - run time in seconds to the last update of this file - - `fuzzer_pid` - PID of the fuzzer process - - `cycles_done` - queue cycles completed so far - - `cycles_wo_finds` - number of cycles without any new paths found - - `execs_done` - number of execve() calls attempted - - `execs_per_sec` - overall number of execs per second - - `paths_total` - total number of entries in the queue - - `paths_favored` - number of queue entries that are favored - - `paths_found` - number of entries discovered through local fuzzing - - `paths_imported` - number of entries imported from other instances - - `max_depth` - number of levels in the generated data set - - `cur_path` - currently processed entry number - - `pending_favs` - number of favored entries still waiting to be fuzzed - - `pending_total` - number of all entries waiting to be fuzzed - - `variable_paths` - number of test cases showing variable behavior - - `stability` - percentage of bitmap bytes that behave consistently - - `bitmap_cvg` - percentage of edge coverage found in the map so far - - `unique_crashes` - number of unique crashes recorded - - `unique_hangs` - number of unique hangs encountered - - `last_path` - seconds since the last path was found - - `last_crash` - seconds since the last crash was found - - `last_hang` - seconds since the last hang was found - - `execs_since_crash` - execs since the last crash was found - - `exec_timeout` - the -t command line value - - `slowest_exec_ms` - real time of the slowest execution in ms - - `peak_rss_mb` - max rss usage reached during fuzzing in MB - - `edges_found` - how many edges have been found - - `var_byte_count` - how many edges are non-deterministic - - `afl_banner` - banner text (e.g. the target name) - - `afl_version` - the version of AFL used - - `target_mode` - default, persistent, qemu, unicorn, non-instrumented - - `command_line` - full command line used for the fuzzing session - -Most of these map directly to the UI elements discussed earlier on. - -On top of that, you can also find an entry called `plot_data`, containing a -plottable history for most of these fields. If you have gnuplot installed, you -can turn this into a nice progress report with the included `afl-plot` tool. - - -### Addendum: Automatically send metrics with StatsD - -In a CI environment or when running multiple fuzzers, it can be tedious to -log into each of them or deploy scripts to read the fuzzer statistics. -Using `AFL_STATSD` (and the other related environment variables `AFL_STATSD_HOST`, -`AFL_STATSD_PORT`, `AFL_STATSD_TAGS_FLAVOR`) you can automatically send metrics -to your favorite StatsD server. Depending on your StatsD server you will be able -to monitor, trigger alerts or perform actions based on these metrics (e.g: alert on -slow exec/s for a new build, threshold of crashes, time since last crash > X, etc). - -The selected metrics are a subset of all the metrics found in the status and in -the plot file. The list is the following: `cycle_done`, `cycles_wo_finds`, -`execs_done`,`execs_per_sec`, `paths_total`, `paths_favored`, `paths_found`, -`paths_imported`, `max_depth`, `cur_path`, `pending_favs`, `pending_total`, -`variable_paths`, `unique_crashes`, `unique_hangs`, `total_crashes`, -`slowest_exec_ms`, `edges_found`, `var_byte_count`, `havoc_expansion`. -Their definitions can be found in the addendum above. - -When using multiple fuzzer instances with StatsD it is *strongly* recommended to setup -the flavor (AFL_STATSD_TAGS_FLAVOR) to match your StatsD server. This will allow you -to see individual fuzzer performance, detect bad ones, see the progress of each -strategy... diff --git a/docs/technical_details.md b/docs/technical_details.md deleted file mode 100644 index 994ffe9f..00000000 --- a/docs/technical_details.md +++ /dev/null @@ -1,550 +0,0 @@ -# Technical "whitepaper" for afl-fuzz - - -NOTE: this document is mostly outdated! - - -This document provides a quick overview of the guts of American Fuzzy Lop. -See README.md for the general instruction manual; and for a discussion of -motivations and design goals behind AFL, see historical_notes.md. - -## 0. Design statement - -American Fuzzy Lop does its best not to focus on any singular principle of -operation and not be a proof-of-concept for any specific theory. The tool can -be thought of as a collection of hacks that have been tested in practice, -found to be surprisingly effective, and have been implemented in the simplest, -most robust way I could think of at the time. - -Many of the resulting features are made possible thanks to the availability of -lightweight instrumentation that served as a foundation for the tool, but this -mechanism should be thought of merely as a means to an end. The only true -governing principles are speed, reliability, and ease of use. - -## 1. Coverage measurements - -The instrumentation injected into compiled programs captures branch (edge) -coverage, along with coarse branch-taken hit counts. The code injected at -branch points is essentially equivalent to: - -```c - cur_location = <COMPILE_TIME_RANDOM>; - shared_mem[cur_location ^ prev_location]++; - prev_location = cur_location >> 1; -``` - -The `cur_location` value is generated randomly to simplify the process of -linking complex projects and keep the XOR output distributed uniformly. - -The `shared_mem[]` array is a 64 kB SHM region passed to the instrumented binary -by the caller. Every byte set in the output map can be thought of as a hit for -a particular (`branch_src`, `branch_dst`) tuple in the instrumented code. - -The size of the map is chosen so that collisions are sporadic with almost all -of the intended targets, which usually sport between 2k and 10k discoverable -branch points: - -``` - Branch cnt | Colliding tuples | Example targets - ------------+------------------+----------------- - 1,000 | 0.75% | giflib, lzo - 2,000 | 1.5% | zlib, tar, xz - 5,000 | 3.5% | libpng, libwebp - 10,000 | 7% | libxml - 20,000 | 14% | sqlite - 50,000 | 30% | - -``` - -At the same time, its size is small enough to allow the map to be analyzed -in a matter of microseconds on the receiving end, and to effortlessly fit -within L2 cache. - -This form of coverage provides considerably more insight into the execution -path of the program than simple block coverage. In particular, it trivially -distinguishes between the following execution traces: - -``` - A -> B -> C -> D -> E (tuples: AB, BC, CD, DE) - A -> B -> D -> C -> E (tuples: AB, BD, DC, CE) -``` - -This aids the discovery of subtle fault conditions in the underlying code, -because security vulnerabilities are more often associated with unexpected -or incorrect state transitions than with merely reaching a new basic block. - -The reason for the shift operation in the last line of the pseudocode shown -earlier in this section is to preserve the directionality of tuples (without -this, A ^ B would be indistinguishable from B ^ A) and to retain the identity -of tight loops (otherwise, A ^ A would be obviously equal to B ^ B). - -The absence of simple saturating arithmetic opcodes on Intel CPUs means that -the hit counters can sometimes wrap around to zero. Since this is a fairly -unlikely and localized event, it's seen as an acceptable performance trade-off. - -### 2. Detecting new behaviors - -The fuzzer maintains a global map of tuples seen in previous executions; this -data can be rapidly compared with individual traces and updated in just a couple -of dword- or qword-wide instructions and a simple loop. - -When a mutated input produces an execution trace containing new tuples, the -corresponding input file is preserved and routed for additional processing -later on (see section #3). Inputs that do not trigger new local-scale state -transitions in the execution trace (i.e., produce no new tuples) are discarded, -even if their overall control flow sequence is unique. - -This approach allows for a very fine-grained and long-term exploration of -program state while not having to perform any computationally intensive and -fragile global comparisons of complex execution traces, and while avoiding the -scourge of path explosion. - -To illustrate the properties of the algorithm, consider that the second trace -shown below would be considered substantially new because of the presence of -new tuples (CA, AE): - -``` - #1: A -> B -> C -> D -> E - #2: A -> B -> C -> A -> E -``` - -At the same time, with #2 processed, the following pattern will not be seen -as unique, despite having a markedly different overall execution path: - -``` - #3: A -> B -> C -> A -> B -> C -> A -> B -> C -> D -> E -``` - -In addition to detecting new tuples, the fuzzer also considers coarse tuple -hit counts. These are divided into several buckets: - -``` - 1, 2, 3, 4-7, 8-15, 16-31, 32-127, 128+ -``` - -To some extent, the number of buckets is an implementation artifact: it allows -an in-place mapping of an 8-bit counter generated by the instrumentation to -an 8-position bitmap relied on by the fuzzer executable to keep track of the -already-seen execution counts for each tuple. - -Changes within the range of a single bucket are ignored; transition from one -bucket to another is flagged as an interesting change in program control flow, -and is routed to the evolutionary process outlined in the section below. - -The hit count behavior provides a way to distinguish between potentially -interesting control flow changes, such as a block of code being executed -twice when it was normally hit only once. At the same time, it is fairly -insensitive to empirically less notable changes, such as a loop going from -47 cycles to 48. The counters also provide some degree of "accidental" -immunity against tuple collisions in dense trace maps. - -The execution is policed fairly heavily through memory and execution time -limits; by default, the timeout is set at 5x the initially-calibrated -execution speed, rounded up to 20 ms. The aggressive timeouts are meant to -prevent dramatic fuzzer performance degradation by descending into tarpits -that, say, improve coverage by 1% while being 100x slower; we pragmatically -reject them and hope that the fuzzer will find a less expensive way to reach -the same code. Empirical testing strongly suggests that more generous time -limits are not worth the cost. - -## 3. Evolving the input queue - -Mutated test cases that produced new state transitions within the program are -added to the input queue and used as a starting point for future rounds of -fuzzing. They supplement, but do not automatically replace, existing finds. - -In contrast to more greedy genetic algorithms, this approach allows the tool -to progressively explore various disjoint and possibly mutually incompatible -features of the underlying data format, as shown in this image: - -  - -Several practical examples of the results of this algorithm are discussed -here: - - https://lcamtuf.blogspot.com/2014/11/pulling-jpegs-out-of-thin-air.html - https://lcamtuf.blogspot.com/2014/11/afl-fuzz-nobody-expects-cdata-sections.html - -The synthetic corpus produced by this process is essentially a compact -collection of "hmm, this does something new!" input files, and can be used to -seed any other testing processes down the line (for example, to manually -stress-test resource-intensive desktop apps). - -With this approach, the queue for most targets grows to somewhere between 1k -and 10k entries; approximately 10-30% of this is attributable to the discovery -of new tuples, and the remainder is associated with changes in hit counts. - -The following table compares the relative ability to discover file syntax and -explore program states when using several different approaches to guided -fuzzing. The instrumented target was GNU patch 2.7k.3 compiled with `-O3` and -seeded with a dummy text file; the session consisted of a single pass over the -input queue with afl-fuzz: - -``` - Fuzzer guidance | Blocks | Edges | Edge hit | Highest-coverage - strategy used | reached | reached | cnt var | test case generated - ------------------+---------+---------+----------+--------------------------- - (Initial file) | 156 | 163 | 1.00 | (none) - | | | | - Blind fuzzing S | 182 | 205 | 2.23 | First 2 B of RCS diff - Blind fuzzing L | 228 | 265 | 2.23 | First 4 B of -c mode diff - Block coverage | 855 | 1,130 | 1.57 | Almost-valid RCS diff - Edge coverage | 1,452 | 2,070 | 2.18 | One-chunk -c mode diff - AFL model | 1,765 | 2,597 | 4.99 | Four-chunk -c mode diff -``` - -The first entry for blind fuzzing ("S") corresponds to executing just a single -round of testing; the second set of figures ("L") shows the fuzzer running in a -loop for a number of execution cycles comparable with that of the instrumented -runs, which required more time to fully process the growing queue. - -Roughly similar results have been obtained in a separate experiment where the -fuzzer was modified to compile out all the random fuzzing stages and leave just -a series of rudimentary, sequential operations such as walking bit flips. -Because this mode would be incapable of altering the size of the input file, -the sessions were seeded with a valid unified diff: - -``` - Queue extension | Blocks | Edges | Edge hit | Number of unique - strategy used | reached | reached | cnt var | crashes found - ------------------+---------+---------+----------+------------------ - (Initial file) | 624 | 717 | 1.00 | - - | | | | - Blind fuzzing | 1,101 | 1,409 | 1.60 | 0 - Block coverage | 1,255 | 1,649 | 1.48 | 0 - Edge coverage | 1,259 | 1,734 | 1.72 | 0 - AFL model | 1,452 | 2,040 | 3.16 | 1 -``` - -At noted earlier on, some of the prior work on genetic fuzzing relied on -maintaining a single test case and evolving it to maximize coverage. At least -in the tests described above, this "greedy" approach appears to confer no -substantial benefits over blind fuzzing strategies. - -### 4. Culling the corpus - -The progressive state exploration approach outlined above means that some of -the test cases synthesized later on in the game may have edge coverage that -is a strict superset of the coverage provided by their ancestors. - -To optimize the fuzzing effort, AFL periodically re-evaluates the queue using a -fast algorithm that selects a smaller subset of test cases that still cover -every tuple seen so far, and whose characteristics make them particularly -favorable to the tool. - -The algorithm works by assigning every queue entry a score proportional to its -execution latency and file size; and then selecting lowest-scoring candidates -for each tuple. - -The tuples are then processed sequentially using a simple workflow: - - 1) Find next tuple not yet in the temporary working set, - 2) Locate the winning queue entry for this tuple, - 3) Register *all* tuples present in that entry's trace in the working set, - 4) Go to #1 if there are any missing tuples in the set. - -The generated corpus of "favored" entries is usually 5-10x smaller than the -starting data set. Non-favored entries are not discarded, but they are skipped -with varying probabilities when encountered in the queue: - - - If there are new, yet-to-be-fuzzed favorites present in the queue, 99% - of non-favored entries will be skipped to get to the favored ones. - - If there are no new favorites: - * If the current non-favored entry was fuzzed before, it will be skipped - 95% of the time. - * If it hasn't gone through any fuzzing rounds yet, the odds of skipping - drop down to 75%. - -Based on empirical testing, this provides a reasonable balance between queue -cycling speed and test case diversity. - -Slightly more sophisticated but much slower culling can be performed on input -or output corpora with `afl-cmin`. This tool permanently discards the redundant -entries and produces a smaller corpus suitable for use with `afl-fuzz` or -external tools. - -## 5. Trimming input files - -File size has a dramatic impact on fuzzing performance, both because large -files make the target binary slower, and because they reduce the likelihood -that a mutation would touch important format control structures, rather than -redundant data blocks. This is discussed in more detail in perf_tips.md. - -The possibility that the user will provide a low-quality starting corpus aside, -some types of mutations can have the effect of iteratively increasing the size -of the generated files, so it is important to counter this trend. - -Luckily, the instrumentation feedback provides a simple way to automatically -trim down input files while ensuring that the changes made to the files have no -impact on the execution path. - -The built-in trimmer in afl-fuzz attempts to sequentially remove blocks of data -with variable length and stepover; any deletion that doesn't affect the checksum -of the trace map is committed to disk. The trimmer is not designed to be -particularly thorough; instead, it tries to strike a balance between precision -and the number of `execve()` calls spent on the process, selecting the block size -and stepover to match. The average per-file gains are around 5-20%. - -The standalone `afl-tmin` tool uses a more exhaustive, iterative algorithm, and -also attempts to perform alphabet normalization on the trimmed files. The -operation of `afl-tmin` is as follows. - -First, the tool automatically selects the operating mode. If the initial input -crashes the target binary, afl-tmin will run in non-instrumented mode, simply -keeping any tweaks that produce a simpler file but still crash the target. -The same mode is used for hangs, if `-H` (hang mode) is specified. -If the target is non-crashing, the tool uses an instrumented mode and keeps only -the tweaks that produce exactly the same execution path. - -The actual minimization algorithm is: - - 1) Attempt to zero large blocks of data with large stepovers. Empirically, - this is shown to reduce the number of execs by preempting finer-grained - efforts later on. - 2) Perform a block deletion pass with decreasing block sizes and stepovers, - binary-search-style. - 3) Perform alphabet normalization by counting unique characters and trying - to bulk-replace each with a zero value. - 4) As a last result, perform byte-by-byte normalization on non-zero bytes. - -Instead of zeroing with a 0x00 byte, `afl-tmin` uses the ASCII digit '0'. This -is done because such a modification is much less likely to interfere with -text parsing, so it is more likely to result in successful minimization of -text files. - -The algorithm used here is less involved than some other test case -minimization approaches proposed in academic work, but requires far fewer -executions and tends to produce comparable results in most real-world -applications. - -## 6. Fuzzing strategies - -The feedback provided by the instrumentation makes it easy to understand the -value of various fuzzing strategies and optimize their parameters so that they -work equally well across a wide range of file types. The strategies used by -afl-fuzz are generally format-agnostic and are discussed in more detail here: - - https://lcamtuf.blogspot.com/2014/08/binary-fuzzing-strategies-what-works.html - -It is somewhat notable that especially early on, most of the work done by -`afl-fuzz` is actually highly deterministic, and progresses to random stacked -modifications and test case splicing only at a later stage. The deterministic -strategies include: - - - Sequential bit flips with varying lengths and stepovers, - - Sequential addition and subtraction of small integers, - - Sequential insertion of known interesting integers (`0`, `1`, `INT_MAX`, etc), - -The purpose of opening with deterministic steps is related to their tendency to -produce compact test cases and small diffs between the non-crashing and crashing -inputs. - -With deterministic fuzzing out of the way, the non-deterministic steps include -stacked bit flips, insertions, deletions, arithmetics, and splicing of different -test cases. - -The relative yields and `execve()` costs of all these strategies have been -investigated and are discussed in the aforementioned blog post. - -For the reasons discussed in historical_notes.md (chiefly, performance, -simplicity, and reliability), AFL generally does not try to reason about the -relationship between specific mutations and program states; the fuzzing steps -are nominally blind, and are guided only by the evolutionary design of the -input queue. - -That said, there is one (trivial) exception to this rule: when a new queue -entry goes through the initial set of deterministic fuzzing steps, and tweaks to -some regions in the file are observed to have no effect on the checksum of the -execution path, they may be excluded from the remaining phases of -deterministic fuzzing - and the fuzzer may proceed straight to random tweaks. -Especially for verbose, human-readable data formats, this can reduce the number -of execs by 10-40% or so without an appreciable drop in coverage. In extreme -cases, such as normally block-aligned tar archives, the gains can be as high as -90%. - -Because the underlying "effector maps" are local every queue entry and remain -in force only during deterministic stages that do not alter the size or the -general layout of the underlying file, this mechanism appears to work very -reliably and proved to be simple to implement. - -## 7. Dictionaries - -The feedback provided by the instrumentation makes it easy to automatically -identify syntax tokens in some types of input files, and to detect that certain -combinations of predefined or auto-detected dictionary terms constitute a -valid grammar for the tested parser. - -A discussion of how these features are implemented within afl-fuzz can be found -here: - - https://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html - -In essence, when basic, typically easily-obtained syntax tokens are combined -together in a purely random manner, the instrumentation and the evolutionary -design of the queue together provide a feedback mechanism to differentiate -between meaningless mutations and ones that trigger new behaviors in the -instrumented code - and to incrementally build more complex syntax on top of -this discovery. - -The dictionaries have been shown to enable the fuzzer to rapidly reconstruct -the grammar of highly verbose and complex languages such as JavaScript, SQL, -or XML; several examples of generated SQL statements are given in the blog -post mentioned above. - -Interestingly, the AFL instrumentation also allows the fuzzer to automatically -isolate syntax tokens already present in an input file. It can do so by looking -for run of bytes that, when flipped, produce a consistent change to the -program's execution path; this is suggestive of an underlying atomic comparison -to a predefined value baked into the code. The fuzzer relies on this signal -to build compact "auto dictionaries" that are then used in conjunction with -other fuzzing strategies. - -## 8. De-duping crashes - -De-duplication of crashes is one of the more important problems for any -competent fuzzing tool. Many of the naive approaches run into problems; in -particular, looking just at the faulting address may lead to completely -unrelated issues being clustered together if the fault happens in a common -library function (say, `strcmp`, `strcpy`); while checksumming call stack -backtraces can lead to extreme crash count inflation if the fault can be -reached through a number of different, possibly recursive code paths. - -The solution implemented in `afl-fuzz` considers a crash unique if any of two -conditions are met: - - - The crash trace includes a tuple not seen in any of the previous crashes, - - The crash trace is missing a tuple that was always present in earlier - faults. - -The approach is vulnerable to some path count inflation early on, but exhibits -a very strong self-limiting effect, similar to the execution path analysis -logic that is the cornerstone of `afl-fuzz`. - -## 9. Investigating crashes - -The exploitability of many types of crashes can be ambiguous; afl-fuzz tries -to address this by providing a crash exploration mode where a known-faulting -test case is fuzzed in a manner very similar to the normal operation of the -fuzzer, but with a constraint that causes any non-crashing mutations to be -thrown away. - -A detailed discussion of the value of this approach can be found here: - - https://lcamtuf.blogspot.com/2014/11/afl-fuzz-crash-exploration-mode.html - -The method uses instrumentation feedback to explore the state of the crashing -program to get past the ambiguous faulting condition and then isolate the -newly-found inputs for human review. - -On the subject of crashes, it is worth noting that in contrast to normal -queue entries, crashing inputs are *not* trimmed; they are kept exactly as -discovered to make it easier to compare them to the parent, non-crashing entry -in the queue. That said, `afl-tmin` can be used to shrink them at will. - -## 10 The fork server - -To improve performance, `afl-fuzz` uses a "fork server", where the fuzzed process -goes through `execve()`, linking, and libc initialization only once, and is then -cloned from a stopped process image by leveraging copy-on-write. The -implementation is described in more detail here: - - https://lcamtuf.blogspot.com/2014/10/fuzzing-binaries-without-execve.html - -The fork server is an integral aspect of the injected instrumentation and -simply stops at the first instrumented function to await commands from -`afl-fuzz`. - -With fast targets, the fork server can offer considerable performance gains, -usually between 1.5x and 2x. It is also possible to: - - - Use the fork server in manual ("deferred") mode, skipping over larger, - user-selected chunks of initialization code. It requires very modest - code changes to the targeted program, and With some targets, can - produce 10x+ performance gains. - - Enable "persistent" mode, where a single process is used to try out - multiple inputs, greatly limiting the overhead of repetitive `fork()` - calls. This generally requires some code changes to the targeted program, - but can improve the performance of fast targets by a factor of 5 or more - approximating the benefits of in-process fuzzing jobs while still - maintaining very robust isolation between the fuzzer process and the - targeted binary. - -## 11. Parallelization - -The parallelization mechanism relies on periodically examining the queues -produced by independently-running instances on other CPU cores or on remote -machines, and then selectively pulling in the test cases that, when tried -out locally, produce behaviors not yet seen by the fuzzer at hand. - -This allows for extreme flexibility in fuzzer setup, including running synced -instances against different parsers of a common data format, often with -synergistic effects. - -For more information about this design, see parallel_fuzzing.md. - -## 12. Binary-only instrumentation - -Instrumentation of black-box, binary-only targets is accomplished with the -help of a separately-built version of QEMU in "user emulation" mode. This also -allows the execution of cross-architecture code - say, ARM binaries on x86. - -QEMU uses basic blocks as translation units; the instrumentation is implemented -on top of this and uses a model roughly analogous to the compile-time hooks: - -```c - if (block_address > elf_text_start && block_address < elf_text_end) { - - cur_location = (block_address >> 4) ^ (block_address << 8); - shared_mem[cur_location ^ prev_location]++; - prev_location = cur_location >> 1; - - } -``` - -The shift-and-XOR-based scrambling in the second line is used to mask the -effects of instruction alignment. - -The start-up of binary translators such as QEMU, DynamoRIO, and PIN is fairly -slow; to counter this, the QEMU mode leverages a fork server similar to that -used for compiler-instrumented code, effectively spawning copies of an -already-initialized process paused at `_start`. - -First-time translation of a new basic block also incurs substantial latency. To -eliminate this problem, the AFL fork server is extended by providing a channel -between the running emulator and the parent process. The channel is used -to notify the parent about the addresses of any newly-encountered blocks and to -add them to the translation cache that will be replicated for future child -processes. - -As a result of these two optimizations, the overhead of the QEMU mode is -roughly 2-5x, compared to 100x+ for PIN. - -## 13. The `afl-analyze` tool - -The file format analyzer is a simple extension of the minimization algorithm -discussed earlier on; instead of attempting to remove no-op blocks, the tool -performs a series of walking byte flips and then annotates runs of bytes -in the input file. - -It uses the following classification scheme: - - - "No-op blocks" - segments where bit flips cause no apparent changes to - control flow. Common examples may be comment sections, pixel data within - a bitmap file, etc. - - "Superficial content" - segments where some, but not all, bitflips - produce some control flow changes. Examples may include strings in rich - documents (e.g., XML, RTF). - - "Critical stream" - a sequence of bytes where all bit flips alter control - flow in different but correlated ways. This may be compressed data, - non-atomically compared keywords or magic values, etc. - - "Suspected length field" - small, atomic integer that, when touched in - any way, causes a consistent change to program control flow, suggestive - of a failed length check. - - "Suspected cksum or magic int" - an integer that behaves similarly to a - length field, but has a numerical value that makes the length explanation - unlikely. This is suggestive of a checksum or other "magic" integer. - - "Suspected checksummed block" - a long block of data where any change - always triggers the same new execution path. Likely caused by failing - a checksum or a similar integrity check before any subsequent parsing - takes place. - - "Magic value section" - a generic token where changes cause the type - of binary behavior outlined earlier, but that doesn't meet any of the - other criteria. May be an atomically compared keyword or so. diff --git a/docs/third_party_tools.md b/docs/third_party_tools.md new file mode 100644 index 00000000..92229e84 --- /dev/null +++ b/docs/third_party_tools.md @@ -0,0 +1,57 @@ +# Tools that help fuzzing with AFL++ + +Speeding up fuzzing: +* [libfiowrapper](https://github.com/marekzmyslowski/libfiowrapper) - if the + function you want to fuzz requires loading a file, this allows using the + shared memory test case feature :-) - recommended. + +Minimization of test cases: +* [afl-pytmin](https://github.com/ilsani/afl-pytmin) - a wrapper for afl-tmin + that tries to speed up the process of minimization of a single test case by + using many CPU cores. +* [afl-ddmin-mod](https://github.com/MarkusTeufelberger/afl-ddmin-mod) - a + variation of afl-tmin based on the ddmin algorithm. +* [halfempty](https://github.com/googleprojectzero/halfempty) - is a fast + utility for minimizing test cases by Tavis Ormandy based on parallelization. + +Distributed execution: +* [disfuzz-afl](https://github.com/MartijnB/disfuzz-afl) - distributed fuzzing + for AFL. +* [AFLDFF](https://github.com/quantumvm/AFLDFF) - AFL distributed fuzzing + framework. +* [afl-launch](https://github.com/bnagy/afl-launch) - a tool for the execution + of many AFL instances. +* [afl-mothership](https://github.com/afl-mothership/afl-mothership) - + management and execution of many synchronized AFL fuzzers on AWS cloud. +* [afl-in-the-cloud](https://github.com/abhisek/afl-in-the-cloud) - another + script for running AFL in AWS. + +Deployment, management, monitoring, reporting +* [afl-utils](https://gitlab.com/rc0r/afl-utils) - a set of utilities for + automatic processing/analysis of crashes and reducing the number of test + cases. +* [afl-other-arch](https://github.com/shellphish/afl-other-arch) - is a set of + patches and scripts for easily adding support for various non-x86 + architectures for AFL. +* [afl-trivia](https://github.com/bnagy/afl-trivia) - a few small scripts to + simplify the management of AFL. +* [afl-monitor](https://github.com/reflare/afl-monitor) - a script for + monitoring AFL. +* [afl-manager](https://github.com/zx1340/afl-manager) - a web server on Python + for managing multi-afl. +* [afl-remote](https://github.com/block8437/afl-remote) - a web server for the + remote management of AFL instances. +* [afl-extras](https://github.com/fekir/afl-extras) - shell scripts to + parallelize afl-tmin, startup, and data collection. + +Crash processing +* [afl-crash-analyzer](https://github.com/floyd-fuh/afl-crash-analyzer) - + another crash analyzer for AFL. +* [fuzzer-utils](https://github.com/ThePatrickStar/fuzzer-utils) - a set of + scripts for the analysis of results. +* [atriage](https://github.com/Ayrx/atriage) - a simple triage tool. +* [afl-kit](https://github.com/kcwu/afl-kit) - afl-cmin on Python. +* [AFLize](https://github.com/d33tah/aflize) - a tool that automatically + generates builds of debian packages suitable for AFL. +* [afl-fid](https://github.com/FoRTE-Research/afl-fid) - a set of tools for + working with input data. \ No newline at end of file diff --git a/docs/tools.md b/docs/tools.md deleted file mode 100644 index ba96d0ce..00000000 --- a/docs/tools.md +++ /dev/null @@ -1,33 +0,0 @@ -# Tools that help fuzzing with AFL++ - -Speeding up fuzzing: - * [libfiowrapper](https://github.com/marekzmyslowski/libfiowrapper) - if the function you want to fuzz requires loading a file, this allows using the shared memory testcase feature :-) - recommended. - -Minimization of test cases: - * [afl-pytmin](https://github.com/ilsani/afl-pytmin) - a wrapper for afl-tmin that tries to speed up the process of minimization of a single test case by using many CPU cores. - * [afl-ddmin-mod](https://github.com/MarkusTeufelberger/afl-ddmin-mod) - a variation of afl-tmin based on the ddmin algorithm. - * [halfempty](https://github.com/googleprojectzero/halfempty) - is a fast utility for minimizing test cases by Tavis Ormandy based on parallelization. - -Distributed execution: - * [disfuzz-afl](https://github.com/MartijnB/disfuzz-afl) - distributed fuzzing for AFL. - * [AFLDFF](https://github.com/quantumvm/AFLDFF) - AFL distributed fuzzing framework. - * [afl-launch](https://github.com/bnagy/afl-launch) - a tool for the execution of many AFL instances. - * [afl-mothership](https://github.com/afl-mothership/afl-mothership) - management and execution of many synchronized AFL fuzzers on AWS cloud. - * [afl-in-the-cloud](https://github.com/abhisek/afl-in-the-cloud) - another script for running AFL in AWS. - -Deployment, management, monitoring, reporting - * [afl-utils](https://gitlab.com/rc0r/afl-utils) - a set of utilities for automatic processing/analysis of crashes and reducing the number of test cases. - * [afl-other-arch](https://github.com/shellphish/afl-other-arch) - is a set of patches and scripts for easily adding support for various non-x86 architectures for AFL. - * [afl-trivia](https://github.com/bnagy/afl-trivia) - a few small scripts to simplify the management of AFL. - * [afl-monitor](https://github.com/reflare/afl-monitor) - a script for monitoring AFL. - * [afl-manager](https://github.com/zx1340/afl-manager) - a web server on Python for managing multi-afl. - * [afl-remote](https://github.com/block8437/afl-remote) - a web server for the remote management of AFL instances. - * [afl-extras](https://github.com/fekir/afl-extras) - shell scripts to parallelize afl-tmin, startup, and data collection. - -Crash processing - * [afl-crash-analyzer](https://github.com/floyd-fuh/afl-crash-analyzer) - another crash analyzer for AFL. - * [fuzzer-utils](https://github.com/ThePatrickStar/fuzzer-utils) - a set of scripts for the analysis of results. - * [atriage](https://github.com/Ayrx/atriage) - a simple triage tool. - * [afl-kit](https://github.com/kcwu/afl-kit) - afl-cmin on Python. - * [AFLize](https://github.com/d33tah/aflize) - a tool that automatically generates builds of debian packages suitable for AFL. - * [afl-fid](https://github.com/FoRTE-Research/afl-fid) - a set of tools for working with input data. \ No newline at end of file diff --git a/docs/triaging_crashes.md b/docs/triaging_crashes.md deleted file mode 100644 index 21ccecaa..00000000 --- a/docs/triaging_crashes.md +++ /dev/null @@ -1,46 +0,0 @@ -# Triaging crashes - -The coverage-based grouping of crashes usually produces a small data set that -can be quickly triaged manually or with a very simple GDB or Valgrind script. -Every crash is also traceable to its parent non-crashing test case in the -queue, making it easier to diagnose faults. - -Having said that, it's important to acknowledge that some fuzzing crashes can be -difficult to quickly evaluate for exploitability without a lot of debugging and -code analysis work. To assist with this task, afl-fuzz supports a very unique -"crash exploration" mode enabled with the -C flag. - -In this mode, the fuzzer takes one or more crashing test cases as the input -and uses its feedback-driven fuzzing strategies to very quickly enumerate all -code paths that can be reached in the program while keeping it in the -crashing state. - -Mutations that do not result in a crash are rejected; so are any changes that -do not affect the execution path. - -The output is a small corpus of files that can be very rapidly examined to see -what degree of control the attacker has over the faulting address, or whether -it is possible to get past an initial out-of-bounds read - and see what lies -beneath. - -Oh, one more thing: for test case minimization, give afl-tmin a try. The tool -can be operated in a very simple way: - -```shell -./afl-tmin -i test_case -o minimized_result -- /path/to/program [...] -``` - -The tool works with crashing and non-crashing test cases alike. In the crash -mode, it will happily accept instrumented and non-instrumented binaries. In the -non-crashing mode, the minimizer relies on standard AFL++ instrumentation to make -the file simpler without altering the execution path. - -The minimizer accepts the -m, -t, -f and @@ syntax in a manner compatible with -afl-fuzz. - -Another tool in AFL++ is the afl-analyze tool. It takes an input -file, attempts to sequentially flip bytes, and observes the behavior of the -tested program. It then color-codes the input based on which sections appear to -be critical, and which are not; while not bulletproof, it can often offer quick -insights into complex file formats. More info about its operation can be found -near the end of [technical_details.md](technical_details.md). \ No newline at end of file diff --git a/docs/tutorials.md b/docs/tutorials.md index cc7ed130..ed8a7eec 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -1,6 +1,6 @@ # Tutorials -Here are some good writeups to show how to effectively use AFL++: +Here are some good write-ups to show how to effectively use AFL++: * [https://aflplus.plus/docs/tutorials/libxml2_tutorial/](https://aflplus.plus/docs/tutorials/libxml2_tutorial/) * [https://bananamafia.dev/post/gb-fuzz/](https://bananamafia.dev/post/gb-fuzz/) @@ -18,9 +18,13 @@ training, then we can highly recommend the following: If you are interested in fuzzing structured data (where you define what the structure is), these links have you covered: -* Superion for AFL++: [https://github.com/adrian-rt/superion-mutator](https://github.com/adrian-rt/superion-mutator) -* libprotobuf for AFL++: [https://github.com/P1umer/AFLplusplus-protobuf-mutator](https://github.com/P1umer/AFLplusplus-protobuf-mutator) -* libprotobuf raw: [https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator](https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator) -* libprotobuf for old AFL++ API: [https://github.com/thebabush/afl-libprotobuf-mutator](https://github.com/thebabush/afl-libprotobuf-mutator) +* Superion for AFL++: + [https://github.com/adrian-rt/superion-mutator](https://github.com/adrian-rt/superion-mutator) +* libprotobuf for AFL++: + [https://github.com/P1umer/AFLplusplus-protobuf-mutator](https://github.com/P1umer/AFLplusplus-protobuf-mutator) +* libprotobuf raw: + [https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator](https://github.com/bruce30262/libprotobuf-mutator_fuzzing_learning/tree/master/4_libprotobuf_aflpp_custom_mutator) +* libprotobuf for old AFL++ API: + [https://github.com/thebabush/afl-libprotobuf-mutator](https://github.com/thebabush/afl-libprotobuf-mutator) If you find other good ones, please send them to us :-) \ No newline at end of file diff --git a/qemu_mode/README.md b/qemu_mode/README.md index d28479d9..c62309a2 100644 --- a/qemu_mode/README.md +++ b/qemu_mode/README.md @@ -217,5 +217,6 @@ them at run time, can be a faster alternative. That said, static rewriting is fraught with peril, because it depends on being able to properly and fully model program control flow without actually executing each and every code path. -Checkout the "Fuzzing binary-only targets" section in our main README.md and -the docs/binaryonly_fuzzing.md document for more information and hints. +Check out +[docs/fuzzing_binary-only_targets.md](../docs/fuzzing_binary-only_targets.md) +for more information and hints. diff --git a/qemu_mode/libqasan/README.md b/qemu_mode/libqasan/README.md index 4a241233..6a65c12b 100644 --- a/qemu_mode/libqasan/README.md +++ b/qemu_mode/libqasan/README.md @@ -19,7 +19,7 @@ finding capabilities during fuzzing) is WIP. ### When should I use QASan? If your target binary is PIC x86_64, you should also give a try to -[retrowrite](https://github.com/HexHive/retrowrite) for static rewriting. +[RetroWrite](https://github.com/HexHive/retrowrite) for static rewriting. If it fails, or if your binary is for another architecture, or you want to use persistent and snapshot mode, AFL++ QASan mode is what you want/have to use. diff --git a/unicorn_mode/samples/persistent/COMPILE.md b/unicorn_mode/samples/persistent/COMPILE.md index 111dfc54..9f2ae718 100644 --- a/unicorn_mode/samples/persistent/COMPILE.md +++ b/unicorn_mode/samples/persistent/COMPILE.md @@ -1,13 +1,16 @@ # C Sample This shows a simple persistent harness for unicornafl in C. -In contrast to the normal c harness, this harness manually resets the unicorn state on each new input. -Thanks to this, we can rerun the testcase in unicorn multiple times, without the need to fork again. +In contrast to the normal c harness, this harness manually resets the unicorn +state on each new input. +Thanks to this, we can rerun the test case in unicorn multiple times, without +the need to fork again. ## Compiling sample.c The target can be built using the `make` command. Just make sure you have built unicorn support first: + ```bash cd /path/to/afl/unicorn_mode ./build_unicorn_support.sh @@ -19,6 +22,7 @@ You don't need to compile persistent_target.c since a X86_64 binary version is pre-built and shipped in this sample folder. This file documents how the binary was built in case you want to rebuild it or recompile it for any reason. -The pre-built binary (persistent_target_x86_64.bin) was built using -g -O0 in gcc. +The pre-built binary (persistent_target_x86_64.bin) was built using -g -O0 in +gcc. -We then load the binary and we execute the main function directly. +We then load the binary and we execute the main function directly. \ No newline at end of file diff --git a/utils/aflpp_driver/README.md b/utils/aflpp_driver/README.md index 30e2412f..4560be2b 100644 --- a/utils/aflpp_driver/README.md +++ b/utils/aflpp_driver/README.md @@ -7,15 +7,15 @@ targets. Just do `afl-clang-fast++ -o fuzz fuzzer_harness.cc libAFLDriver.a [plus required linking]`. -You can also sneakily do this little trick: +You can also sneakily do this little trick: If this is the clang compile command to build for libfuzzer: `clang++ -o fuzz -fsanitize=fuzzer fuzzer_harness.cc -lfoo` then just switch `clang++` with `afl-clang-fast++` and our compiler will magically insert libAFLDriver.a :) -To use shared-memory testcases, you need nothing to do. -To use stdin testcases give `-` as the only command line parameter. -To use file input testcases give `@@` as the only command line parameter. +To use shared-memory test cases, you need nothing to do. +To use stdin test cases, give `-` as the only command line parameter. +To use file input test cases, give `@@` as the only command line parameter. IMPORTANT: if you use `afl-cmin` or `afl-cmin.bash` then either pass `-` or `@@` as command line parameters. @@ -30,8 +30,8 @@ are to be fuzzed in qemu_mode. So we compile them with clang/clang++, without `clang++ -o fuzz fuzzer_harness.cc libAFLQemuDriver.a [plus required linking]`. - Then just do (where the name of the binary is `fuzz`): + ``` AFL_QEMU_PERSISTENT_ADDR=0x$(nm fuzz | grep "T LLVMFuzzerTestOneInput" | awk '{print $1}') AFL_QEMU_PERSISTENT_HOOK=/path/to/aflpp_qemu_driver_hook.so afl-fuzz -Q ... -- ./fuzz` @@ -40,4 +40,4 @@ AFL_QEMU_PERSISTENT_HOOK=/path/to/aflpp_qemu_driver_hook.so afl-fuzz -Q ... -- . if you use afl-cmin or `afl-showmap -C` with the aflpp_qemu_driver you need to set the set same AFL_QEMU_... (or AFL_FRIDA_...) environment variables. If you want to use afl-showmap (without -C) or afl-cmin.bash then you may not -set these environment variables and rather set `AFL_QEMU_DRIVER_NO_HOOK=1`. +set these environment variables and rather set `AFL_QEMU_DRIVER_NO_HOOK=1`. \ No newline at end of file |