1 files changed, 143 insertions, 0 deletions
diff --git a/docs/historical_notes.md b/docs/historical_notes.md
new file mode 100644
index 00000000..2079698b
--- /dev/null
+++ b/docs/historical_notes.md
@@ -0,0 +1,143 @@
+# Historical notes
+
+  This doc talks about the rationale of some of the high-level design decisions
+  for American Fuzzy Lop. It's adopted from a discussion with Rob Graham.
+  See README.md for the general instruction manual, and technical_details.md for
+  additional implementation-level insights.
+
+## 1) Influences
+
+In short, `afl-fuzz` is inspired chiefly by the work done by Tavis Ormandy back
+in 2007. Tavis did some very persuasive experiments using `gcov` block coverage
+to select optimal test cases out of a large corpus of data, and then using
+them as a starting point for traditional fuzzing workflows.
+
+(By "persuasive", I mean: netting a significant number of interesting
+vulnerabilities.)
+
+In parallel to this, both Tavis and I were interested in evolutionary fuzzing.
+Tavis had his experiments, and I was working on a tool called bunny-the-fuzzer,
+released somewhere in 2007.
+
+Bunny used a generational algorithm not much different from `afl-fuzz`, but
+also tried to reason about the relationship between various input bits and
+the internal state of the program, with hopes of deriving some additional value
+from that. The reasoning / correlation part was probably in part inspired by
+other projects done around the same time by Will Drewry and Chris Evans.
+
+The state correlation approach sounded very sexy on paper, but ultimately, made
+the fuzzer complicated, brittle, and cumbersome to use; every other target
+program would require a tweak or two. Because Bunny didn't fare a whole lot
+better than less sophisticated brute-force tools, I eventually decided to write
+it off. You can still find its original documentation at:
+
+  https://code.google.com/p/bunny-the-fuzzer/wiki/BunnyDoc
+
+There has been a fair amount of independent work, too. Most notably, a few
+weeks earlier that year, Jared DeMott had a Defcon presentation about a
+coverage-driven fuzzer that relied on coverage as a fitness function.
+
+Jared's approach was by no means identical to what afl-fuzz does, but it was in
+the same ballpark. His fuzzer tried to explicitly solve for the maximum coverage
+with a single input file; in comparison, afl simply selects for cases that do
+something new (which yields better results - see technical_details.txt).
+
+A few years later, Gabriel Campana released fuzzgrind, a tool that relied purely
+on Valgrind and a constraint solver to maximize coverage without any brute-force
+bits; and Microsoft Research folks talked extensively about their still
+non-public, solver-based SAGE framework.
+
+In the past six years or so, I've also seen a fair number of academic papers
+that dealt with smart fuzzing (focusing chiefly on symbolic execution) and a
+couple papers that discussed proof-of-concept applications of genetic
+algorithms with the same goals in mind. I'm unconvinced how practical most of
+these experiments were; I suspect that many of them suffer from the
+bunny-the-fuzzer's curse of being cool on paper and in carefully designed
+experiments, but failing the ultimate test of being able to find new,
+worthwhile security bugs in otherwise well-fuzzed, real-world software.
+
+In some ways, the baseline that the "cool" solutions have to compete against is
+a lot more impressive than it may seem, making it difficult for competitors to
+stand out. For a singular example, check out the work by Gynvael and Mateusz
+Jurczyk, applying "dumb" fuzzing to ffmpeg, a prominent and security-critical
+component of modern browsers and media players:
+
+  http://googleonlinesecurity.blogspot.com/2014/01/ffmpeg-and-thousand-fixes.html
+
+Effortlessly getting comparable results with state-of-the-art symbolic execution
+in equally complex software still seems fairly unlikely, and hasn't been
+demonstrated in practice so far.
+
+But I digress; ultimately, attribution is hard, and glorying the fundamental
+concepts behind AFL is probably a waste of time. The devil is very much in the
+often-overlooked details, which brings us to...
+
+## 2. Design goals for afl-fuzz
+
+In short, I believe that the current implementation of afl-fuzz takes care of
+several itches that seemed impossible to scratch with other tools:
+
+1) Speed. It's genuinely hard to compete with brute force when your "smart"
+   approach is resource-intensive. If your instrumentation makes it 10x more
+   likely to find a bug, but runs 100x slower, your users are getting a bad
+   deal.
+
+   To avoid starting with a handicap, `afl-fuzz` is meant to let you fuzz most of
+   the intended targets at roughly their native speed - so even if it doesn't
+   add value, you do not lose much.
+
+   On top of this, the tool leverages instrumentation to actually reduce the
+   amount of work in a couple of ways: for example, by carefully trimming the
+   corpus or skipping non-functional but non-trimmable regions in the input
+   files.
+
+2) Rock-solid reliability. It's hard to compete with brute force if your
+   approach is brittle and fails unexpectedly. Automated testing is attractive
+   because it's simple to use and scalable; anything that goes against these
+   principles is an unwelcome trade-off and means that your tool will be used
+   less often and with less consistent results.
+
+   Most of the approaches based on symbolic execution, taint tracking, or
+   complex syntax-aware instrumentation are currently fairly unreliable with
+   real-world targets. Perhaps more importantly, their failure modes can render
+   them strictly worse than "dumb" tools, and such degradation can be difficult
+   for less experienced users to notice and correct.
+
+   In contrast, `afl-fuzz` is designed to be rock solid, chiefly by keeping it
+   simple. In fact, at its core, it's designed to be just a very good
+   traditional fuzzer with a wide range of interesting, well-researched
+   strategies to go by. The fancy parts just help it focus the effort in
+   places where it matters the most.
+
+3) Simplicity. The author of a testing framework is probably the only person
+   who truly understands the impact of all the settings offered by the tool -
+   and who can dial them in just right. Yet, even the most rudimentary fuzzer
+   frameworks often come with countless knobs and fuzzing ratios that need to
+   be guessed by the operator ahead of the time. This can do more harm than 
+   good.
+
+   AFL is designed to avoid this as much as possible. The three knobs you
+   can play with are the output file, the memory limit, and the ability to
+   override the default, auto-calibrated timeout. The rest is just supposed to
+   work. When it doesn't, user-friendly error messages outline the probable
+   causes and workarounds, and get you back on track right away.
+
+4) Chainability. Most general-purpose fuzzers can't be easily employed
+   against resource-hungry or interaction-heavy tools, necessitating the
+   creation of custom in-process fuzzers or the investment of massive CPU
+   power (most of which is wasted on tasks not directly related to the code
+   we actually want to test).
+
+   AFL tries to scratch this itch by allowing users to use more lightweight
+   targets (e.g., standalone image parsing libraries) to create small
+   corpora of interesting test cases that can be fed into a manual testing
+   process or a UI harness later on.
+
+As mentioned in technical_details.txt, AFL does all this not by systematically
+applying a single overarching CS concept, but by experimenting with a variety
+of small, complementary methods that were shown to reliably yields results
+better than chance. The use of instrumentation is a part of that toolkit, but is
+far from being the most important one.
+
+Ultimately, what matters is that `afl-fuzz` is designed to find cool bugs - and
+has a pretty robust track record of doing just that.