NEZHA is an evolutionary-based efficient and domain-independent differential testing framework developed at Columbia University. NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger semantic bugs. NEZHA features several runtime diversity-promoting metrics used to generate inputs for multi-app differential testing.[…]
syzkaller is an unsupervised, coverage-guided Linux syscall fuzzer. It is meant to be used with KASAN (CONFIG_KASAN=y), KTSAN (CONFIG_KTSAN=y), or KUBSAN.
This is a set of tests (benchmarks) for fuzzing engines (fuzzers). The goal of this project is to have a set of fuzzing benchmakrs derived from real-life libraries that have interesting bugs, hard-to-find code paths, or other challenges for bug finding tools. The current version supports libFuzzer, in future versions we exect to support AFL and potentially other fuzzers. […]
“KernelFuzzer: This is the core Kernel Fuzzer, with example library calls and Syscalls to start fuzzing Windows. The fuzzer has been tested on Windows 7 / 10, OS X and QNX. […] See our Def Con 24 slides over at MWR Labs which give an explanation of the fuzzer and examples of writing library calls and syscalls for the fuzzer. One of each is provided as an example and more examples are provided in the slides. […]”
“I am happy to announce today the public release of our evolutionary knowledge-based fuzzer, Choronzon. An overview of the architecture of Choronzon was initially presented at the ZeroNights 2015 conference. A recording of the presentation and the slide deck are also available. You can now find the full source code of Choronzon on the official CENSUS GitHub page. We welcome any feedback you may have and pull requests!”
Excerpt from readme:
Original AFL code written by Michal Zalewski <email@example.com>
Windows fork written and maintained by Ivan Fratric <firstname.lastname@example.org>
Copyright 2016 Google Inc. All Rights Reserved.
[…] Unfortunately, the original AFL does not work on Windows due to very *nix-specific design (e.g. instrumentation, forkserver etc). This project is a fork of AFL that uses different instrumentation approach which works on Windows even for black box binary fuzzing. […] The WinAFL approach: Instead of instrumenting the code at compilation time, WinAFL relies on dynamic instrumentation using DynamoRIO to measure and extract target coverage. This approach has been found to introduce an overhead about 2x compared to the native execution speed, which is comparable to the original AFL in binary instrumentation mode. To improve the process startup time, WinAFL relies heavily on persistant fuzzing mode, that is, executing multiple input samples without restarting the target process. This is accomplished by selecting a target function (that the user wants to fuzz) and instrumenting it so that it runs in a loop. […]
vusbf-Framework: A KVM/QEMU based USB-fuzzing framework.
Sergej Schumilo, OpenSource Security Spenneberg 2015
A USB-fuzzer which takes advantage of massive usage of virtual machines and also offers high reproducibility. This framework was initially released at Black Hat Europe 2014. This software is licensed under GPLv2. vUSBf was written in Python2 and requires the Scapy-framework. This framework provides:
* USB-fuzzing in practical time frames
* multiprocessing and clustering
* export sequences of payloads and replay them for debugging or investigation
* XML-based dynamic testcase generating
* expandable by writing new testcases, USB-emulators or monitoring-modules