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docs/guided_fuzzing.md

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-# Challenges of guided fuzzing
-
-Fuzzing is one of the most powerful and proven strategies for identifying
-security issues in real-world software; it is responsible for the vast
-majority of remote code execution and privilege escalation bugs found to date
-in security-critical software.
-
-Unfortunately, fuzzing is also relatively shallow; blind, random mutations
-make it very unlikely to reach certain code paths in the tested code, leaving
-some vulnerabilities firmly outside the reach of this technique.
-
-There have been numerous attempts to solve this problem. One of the early
-approaches - pioneered by Tavis Ormandy - is corpus distillation. The method
-relies on coverage signals to select a subset of interesting seeds from a
-massive, high-quality corpus of candidate files, and then fuzz them by
-traditional means. The approach works exceptionally well but requires such
-a corpus to be readily available. In addition, block coverage measurements
-provide only a very simplistic understanding of the program state and are less
-useful for guiding the fuzzing effort in the long haul.
-
-Other, more sophisticated research has focused on techniques such as program
-flow analysis ("concolic execution"), symbolic execution, or static analysis.
-All these methods are extremely promising in experimental settings, but tend
-to suffer from reliability and performance problems in practical uses - and
-currently do not offer a viable alternative to "dumb" fuzzing techniques.