LibAFL: A Framework to Build Modular and Reusable Fuzzers
Andrea Fioraldi, Dominik Christian Maier, Dongjia Zhang, Davide Balzarotti
摘要
The release of AFL marked an important milestone in the area of software security testing, revitalizing fuzzing as a major research topic and spurring a large number of research studies that attempted to improve and evaluate the different aspects of the fuzzing pipeline. Many of these studies implemented their techniques by forking the AFL codebase. While this choice might seem appropriate at first, combining multiple forks into a single fuzzer requires a high engineering overhead, which hinders progress in the area and prevents fair and objective evaluations of different techniques. The highly fragmented landscape of the fuzzing ecosystem also prevents researchers from combining orthogonal techniques and makes it difficult for end users to adopt new prototype solutions. To tackle this problem, in this paper we propose LibAFL, a framework to build modular and reusable fuzzers. We discuss the different components generally used in fuzzing and map them to an extensible framework. LibAFL allows researchers and engineers to extend the core fuzzer pipeline and share their new components for further evaluations. As part of LibAFL, we integrated techniques from more than 20 previous works and conduct extensive experiments to show the benefit of our framework to combine and evaluate different approaches. We hope this can help to shed light on current advancements in fuzzing and provide a solid base for comparative and extensible research in the future.
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引用它的顶会 Paper40
- LLM-Fuzzer: Scaling Assessment of Large Language Model JailbreaksJiahao Yu, Xingwei Lin, Zheng Yu, Xinyu XingUSENIX Security 2024 · 被引用 83 次
- ItyFuzz: Snapshot-Based Fuzzer for Smart ContractChaofan Shou, Shangyin Tan, Koushik SenISSTA 2023 · 被引用 76 次
- ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLPLu Yan, Zhuo Zhang, Guanhong Tao, Kaiyuan Zhang 等NeurIPS 2023 · 被引用 37 次
- DY Fuzzing: Formal Dolev-Yao Models Meet Cryptographic Protocol Fuzz TestingMax Ammann, Lucca Hirschi, Steve KremerS&P 2024 · 被引用 25 次
- MultiFuzz: A Multi-Stream Fuzzer For Testing Monolithic FirmwareMichael Chesser, Surya Nepal, Damith C. RanasingheUSENIX Security 2024 · 被引用 13 次
它引用的顶会 Paper26
- Coverage-based Greybox Fuzzing as Markov ChainMarcel Böhme, Van-Thuan Pham, Abhik RoychoudhuryCCS 2016 · 被引用 1,026 次
- Directed Greybox FuzzingMarcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen, Abhik RoychoudhuryCCS 2017 · 被引用 836 次
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 次
- REDQUEEN: Fuzzing with Input-to-State CorrespondenceCornelius Aschermann, Sergej Schumilo, Tim Blazytko, Robert Gawlik 等NDSS 2019 · 被引用 413 次
- Hawkeye: Towards a Desired Directed Grey-box FuzzerHongxu Chen, Yinxing Xue, Yuekang Li, Bihuan Chen 等CCS 2018 · 被引用 335 次
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