ClusterFuzz Integrations

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OSS-Fuzz offers continuous fuzzing for open source software. Fuzz testing is a well-known technique for uncovering programming errors in software. Many of these detectable errors, like buffer overflow, can have serious security implications. Google has found thousands of security vulnerabilities and stability bugs by deploying guided in-process fuzzing of Chrome components, and we now want to share that service with the open source community. OSS-Fuzz aims to make common open source software more secure and stable by combining modern fuzzing techniques with scalable, distributed execution. Projects that do not qualify for OSS-Fuzz can run their own instances of ClusterFuzz or ClusterFuzzLite. Currently, OSS-Fuzz supports C/C++, Rust, Go, Python, and Java/JVM code. Other languages supported by LLVM may work too. OSS-Fuzz supports fuzzing x86_64 and i386 builds.

LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine. LibFuzzer is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entry point (or target function); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage. The code coverage information for libFuzzer is provided by LLVM’s SanitizerCoverage instrumentation. LibFuzzer is still fully supported in that important bugs will get fixed. The first step in using libFuzzer on a library is to implement a fuzz target, a function that accepts an array of bytes and does something interesting with these bytes using the API under test. Note that this fuzz target does not depend on libFuzzer in any way so it is possible and even desirable to use it with other fuzzing engines like AFL and/or Radamsa.

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.

Honggfuzz is a security-oriented software fuzzer. Supports evolutionary, feedback-driven fuzzing based on code coverage (SW and HW-based). It’s multi-process and multi-threaded, there’s no need to run multiple copies of your fuzzer, as Honggfuzz can unlock the potential of all your available CPU cores with a single running instance. The file corpus is automatically shared and improved between all fuzzed processes. It’s blazingly fast when the persistent fuzzing mode is used. A simple/empty LLVMFuzzerTestOneInput function can be tested with up to 1mo iteration per second on a relatively modern CPU. Has a solid track record of uncovered security bugs, the only (to date) vulnerability in OpenSSL with the critical score mark was discovered by Honggfuzz. As opposed to other fuzzers, it will discover and report hijacked/ignored signals from crashes (intercepted and potentially hidden by a fuzzed program).