go-fuzz
Go-fuzz is a coverage-guided fuzzing solution for testing Go packages. Fuzzing is mainly applicable to packages that parse complex inputs (both text and binary) and is especially useful for hardening systems that parse inputs from potentially malicious users (anything accepted over a network). go-fuzz has recently added preliminary support for fuzzing Go Modules. If you encounter a problem with modules, please file an issue with details. Data is a random input generated by go-fuzz, note that in most cases it is invalid. The function must return 1 if the fuzzer should increase the priority of the given input during subsequent fuzzing if the input must not be added to the corpus even if it gives new coverage, and 0 otherwise; other values are reserved for future use. The fuzz function must be in a package that go-fuzz can import. This means the code you want to test can't be in package main. Fuzzing internal packages is supported, however.
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american fuzzy lop
American fuzzy lop is a security-oriented fuzzer that employs a novel type of compile-time instrumentation and genetic algorithms to automatically discover clean, interesting test cases that trigger new internal states in the targeted binary. This substantially improves the functional coverage for the fuzzed code. The compact synthesized corpora produced by the tool are also useful for seeding other, more labor or resource-intensive testing regimes down the road. Compared to other instrumented fuzzers, afl-fuzz is designed to be practical, it has a modest performance overhead, uses a variety of highly effective fuzzing strategies and effort minimization tricks, requires essentially no configuration, and seamlessly handles complex, real-world use cases, say, common image parsing or file compression libraries. It's an instrumentation-guided genetic fuzzer capable of synthesizing complex file semantics in a wide range of non-trivial targets.
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Atheris
Atheris is a coverage-guided Python fuzzing engine. It supports fuzzing of Python code, but also native extensions written for CPython. Atheris is based on libFuzzer. When fuzzing native code, Atheris can be used to catch extra bugs. Atheris supports Linux (32- and 64-bit) and Mac OS X, with Python versions 3.6-3.10. It comes with a built-in libFuzzer, which is fine for fuzzing Python code. If you plan to fuzz native extensions, you may need to build from source to ensure the libFuzzer version in Atheris matches your Clang version. Atheris relies on libFuzzer, which is distributed with Clang. Apple Clang doesn't come with libFuzzer, so you'll need to install a new version of LLVM. Atheris is based on a coverage-guided mutation-based fuzzer (LibFuzzer). This has the advantage of not requiring any grammar definition for generating inputs, making its setup easier. The disadvantage is that it will be harder for the fuzzer to generate inputs for code that parses complex data types.
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Awesome Fuzzing
Awesome Fuzzing is a list of fuzzing resources including books, courses, both free and paid, videos, tools, tutorials, and vulnerable applications to practice in order to learn fuzzing and initial phases of exploit development like root cause analysis. Courses/training videos on fuzzing, videos talking about fuzzing techniques, tools, and best practices. Conference talks and tutorials, blogs, tools that help in fuzzing applications, and fuzzers that help in fuzzing applications that use network-based protocols like HTTP, SSH, SMTP, etc. Search and pick the exploits, that have respective apps available for download, and reproduce the exploit by using the fuzzer of your choice. Set of tests for fuzzing engines. Includes different well-known bugs. A corpus, including various file formats for fuzzing multiple targets in the fuzzing literature.
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