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# Best practices
## Contents
### 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)
### Improvements
* [Improving speed](#improving-speed)
* [Improving stability](#improving-stability)
## Targets
### Fuzzing a binary-only target
For a comprehensive guide, see [binaryonly_fuzzing.md](binaryonly_fuzzing.md).
### Fuzzing a GUI program
If the GUI program can read the fuzz data from a file (via the command line, a fixed location or via an environment variable) without needing any user interaction, then it would be suitable for fuzzing.
Otherwise, it is not possible without modifying the source code - which is a very good idea anyway as the GUI functionality is a huge CPU/time overhead for the fuzzing.
So create a new `main()` that just reads the test case and calls the functionality for processing the input that the GUI program is using.
### Fuzzing a network service
Fuzzing a network service does not work "out of the box".
Using a network channel is inadequate for several reasons:
- it has a slow-down of x10-20 on the fuzzing speed
- it does not scale to fuzzing multiple instances easily,
- instead of one initial data packet often a back-and-forth interplay of packets is needed for stateful protocols (which is totally unsupported by most coverage aware fuzzers).
The established method to fuzz network services is to modify the source code
to read from a file or stdin (fd 0) (or even faster via shared memory, combine
this with persistent mode [instrumentation/README.persistent_mode.md](../instrumentation/README.persistent_mode.md)
and you have a performance gain of x10 instead of a performance loss of over
x10 - that is a x100 difference!).
If modifying the source is not an option (e.g. because you only have a binary
and perform binary fuzzing) you can also use a shared library with AFL_PRELOAD
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There is an outdated AFL++ branch that implements networking if you are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) -
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.
## Improvements
### Improving speed
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).
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).
### Improving stability
For fuzzing a 100% stable target that covers all edges is the best case.
A 90% stable target that covers all edges is however better than a 100% stable target that ignores 10% of the edges.
With instability, you basically have a partial coverage loss on an edge, with ignored functions you have a full loss on that edges.
There are functions that are unstable, but also provide value to coverage, e.g., init functions that use fuzz data as input.
If however a function that has nothing to do with the input data is the source of instability, e.g., checking jitter, or is a hash map function etc., then it should not be instrumented.
To be able to exclude these functions (based on AFL++'s measured stability), the following process will allow to identify functions with variable edges.
Four steps are required to do this and it also requires quite some knowledge of coding and/or disassembly and is effectively possible only with `afl-clang-fast` `PCGUARD` and `afl-clang-lto` `LTO` instrumentation.
1. Instrument to be able to find the responsible function(s):
a) For LTO instrumented binaries, this can be documented during compile time, just set `export AFL_LLVM_DOCUMENT_IDS=/path/to/a/file`.
This file will have one assigned edge ID and the corresponding function per line.
b) For PCGUARD instrumented binaries, it is much more difficult. Here you can either modify the `__sanitizer_cov_trace_pc_guard` function in `instrumentation/afl-llvm-rt.o.c` to write a backtrace to a file if the ID in `__afl_area_ptr[*guard]` is one of the unstable edge IDs.
(Example code is already there).
Then recompile and reinstall `llvm_mode` and rebuild your target.
Run the recompiled target with `afl-fuzz` for a while and then check the file that you wrote with the backtrace information.
Alternatively, you can use `gdb` to hook `__sanitizer_cov_trace_pc_guard_init` on start, check to which memory address the edge ID value is written, and set a write breakpoint to that address (`watch 0x.....`).
c) In other instrumentation types, this is not possible.
So just recompile with the two mentioned above.
This is just for identifying the functions that have unstable edges.
2. Identify which edge ID numbers are unstable.
Run the target with `export AFL_DEBUG=1` for a few minutes then terminate.
The out/fuzzer_stats file will then show the edge IDs that were identified
as unstable in the `var_bytes` entry. You can match these numbers
directly to the data you created in the first step.
Now you know which functions are responsible for the instability
3. Create a text file with the filenames/functions
Identify which source code files contain the functions that you need to remove from instrumentation, or just specify the functions you want to skip for instrumentation.
Note that optimization might inline functions!
Follow this document on how to do this: [instrumentation/README.instrument_list.md](../instrumentation/README.instrument_list.md).
If `PCGUARD` is used, then you need to follow this guide (needs llvm 12+!):
[https://clang.llvm.org/docs/SanitizerCoverage.html#partially-disabling-instrumentation](https://clang.llvm.org/docs/SanitizerCoverage.html#partially-disabling-instrumentation)
Only exclude those functions from instrumentation that provide no value for coverage - that is if it does not process any fuzz data directly or indirectly (e.g. hash maps, thread management etc.).
If however a function directly or indirectly handles fuzz data, then you should not put the function in a deny instrumentation list and rather live with the instability it comes with.
4. Recompile the target
Recompile, fuzz it, be happy :)
This link explains this process for [Fuzzbench](https://github.com/google/fuzzbench/issues/677).
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