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A curated list of awesome directed fuzzing research papers
| Date | Stars |
|---|---|
| 2026-07-24 | 590 |
| 2026-07-25 | 590 |
| 2026-07-28 | 590 |
| 2026-07-30 | 590 |
| 2026-08-06 | 590 |
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# awesome-directed-fuzzing [](https://github.com/sindresorhus/awesome) Directed Fuzzing seems to be a current hot research topic. This repository aims to provide a curated list of research papers focusing on directed greybox fuzzing (see more [directed whitebox fuzzing](./whitebox.md) and [miscellaneous](./misc.md)). -------------------------------------------------------------------------------------------------------------------------- ### [CCS'17] Directed Greybox Fuzzing [[paper]](https://mboehme.github.io/paper/CCS17.pdf) [[project]](https://github.com/aflgo) [[slides]](https://www.slideshare.net/mboehme/aflgo-directed-greybox-fuzzing) [[talk]](https://www.youtube.com/watch?v=jiECNix0HuQ) <details> <summary>Click to see the abstract!</summary> Existing Greybox Fuzzers (GF) cannot be effectively directed, for instance, towards problematic changes or patches, towards critical system calls or dangerous locations, or towards functions in the stacktrace of a reported vulnerability that we wish to reproduce. In this paper, we introduce Directed Greybox Fuzzing (DGF) which generates inputs with the objective of reaching a given set of target program locations efficiently. We develop and evaluate a simulated annealing-based power schedule that gradually assigns more energy to seeds that are closer to the target locations while reducing energy for seeds that are further away. Experiments with our implementation AFLGo demonstrate that DGF outperforms both directed symbolic-execution-based whitebox fuzzing and undirected greybox fuzzing. We show applications of DGF to patch testing and crash reproduction, and discuss the integration of AFLGo into Google’s continuous fuzzing platform OSS-Fuzz. Due to its directedness, AFLGo could find 39 bugs in several well-fuzzed, security-critical projects like LibXML2. 17 CVEs were assigned. </details> -------------------------------------------------------------------------------------------------------------------------- ### [CCS'18] Hawkeye: Towards a Desired Directed Grey-box Fuzzer [[paper]](https://hongxuchen.github.io/pdf/hawkeye.pdf) [[project]](https://sites.google.com/view/fot-the-fuzzer/DGF?authuser=0) [[slides]](https://hongxuchen.github.io/pdf/hawkeye-slides.pdf) [[talk]](https://www.youtube.com/watch?v=BSPj7GAQt5U&list=PLn0nrSd4xjjbyUeai0oevMrT8_IwnBo4R&index=7) <details> <summary>Click to see the abstract!</summary> Grey-box fuzzing is a practically effective approach to test realworld programs. However, most existing grey-box fuzzers lack directedness, i.e. the capability of executing towards user-specified target sites in the program. To emphasize existing challenges in directed fuzzing, we propose Hawkeye to feature four desired properties of directed grey-box fuzzers. Owing to a novel static analysis on the program under test and the target sites, Hawkeye precisely collects the information such as the call graph, function and basic block level distances to the targets. During fuzzing, Hawkeye evaluates exercised seeds based on both static information and the execution traces to generate the dynamic metrics, which are then used for seed prioritization, power scheduling and adaptive mutating. These strategies help Hawkeye to achieve better directedness and gravitate towards the target sites. We implemented Hawkeye as a fuzzing framework and evaluated it on various real-world programs under different scenarios. The experimental results showed that Hawkeye can reach the target sites and reproduce the crashes much faster than state-of-the-art grey-box fuzzers such as AFL and AFLGo. Specially, Hawkeye can reduce the time to exposure for certain vulnerabilities from about 3.5 hours to 0.5 hour. By now, Hawkeye has detected more than 41 previously unknown crashes in projects such as Oniguruma, MJS with the target sites provided by vulnerability prediction tools;
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matched fp:ae45d54b8465a951, topic:awesome, topic:awesome-list, desc:curated list