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Jonas Zeunert
2025-07-18 22:22:32 +02:00
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 Awesome Fuzzing !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
 Awesome Fuzzing !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
▐ Fuzzing (https://en.wikipedia.org/wiki/Fuzzing) or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. The program is then monitored for 
▐ exceptions such as crashes, failing built-in code assertions, or potential memory leaks. Typically, fuzzers are used to test programs that take structured inputs. 
▐ Fuzzing (https://en.wikipedia.org/wiki/Fuzzing) or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer program. The program is then monitored for exceptions
▐ such as crashes, failing built-in code assertions, or potential memory leaks. Typically, fuzzers are used to test programs that take structured inputs. 
A curated list of references to awesome Fuzzing for security testing. Additionally there is a collection of freely available academic papers, tools and so on.
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Tools
Information about the various open source tools you can use to leverage fuzz testing. The items in this section have been organized and classified based on the standards set by the https://fuzzing-survey.org/ website. Although there are
currently more than 35 categories, we have selected the most relevant ones to provide efficient information. Additionally, items that are outdated and deprecated have been excluded, and only those that are currently usable are listed.
Information about the various open source tools you can use to leverage fuzz testing. The items in this section have been organized and classified based on the standards set by the https://fuzzing-survey.org/ website. Although there are currently
more than 35 categories, we have selected the most relevant ones to provide efficient information. Additionally, items that are outdated and deprecated have been excluded, and only those that are currently usable are listed.
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- AFL++ (https://github.com/AFLplusplus/AFLplusplus) - AFL++ is a superior fork to Google's AFL - more speed, more and better mutations, more and better instrumentation, custom module support, etc.
- Angora (https://github.com/AngoraFuzzer/Angora) - Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.
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Network
API
- IvySyn (https://gitlab.com/brown-ssl/ivysyn) - IvySyn is a fully-automated framework for discovering memory error vulnerabilities in Deep Learning (DL) frameworks.
- MINER (https://github.com/puppet-meteor/MINER) - MINER is a REST API fuzzer that utilizes three data-driven designs working together to guide the sequence generation, improve the request generation quality, and capture the unique 
errors caused by incorrect parameter usage.
- MINER (https://github.com/puppet-meteor/MINER) - MINER is a REST API fuzzer that utilizes three data-driven designs working together to guide the sequence generation, improve the request generation quality, and capture the unique errors caused 
by incorrect parameter usage.
- RestTestGen (https://github.com/SeUniVr/RestTestGen) - RestTestGen is a robust tool and framework designed for automated black-box testing of RESTful web APIs.
- GraphFuzz (https://github.com/ForAllSecure/GraphFuzz) - GraphFuzz is an experimental framework for building structure-aware, library API fuzzers.
- Minerva (https://github.com/ChijinZ/Minerva) - Minerva is a browser fuzzer augmented by API mod-ref relations, aiming to synthesize highly-relevant browser API invocations in each test case.
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To the extent possible under law, cpuu has waived all copyright and
related or neighboring rights to this work.
fuzzing Github: https://github.com/cpuu/awesome-fuzzing