An Empirical Study of OSS-Fuzz Bugs
Not on the map. Why?
Abstract (arXiv)
Continuous fuzzing is an increasingly popular technique for automated quality and security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service for open source software. We conduct the first empirical study of OSS-Fuzz, analyzing 23,907 bugs found in 316 projects. We examine the characteristics of fuzzer-found faults, the lifecycles of such faults, and the evolution of fuzzing campaigns over time. We find that OSS-Fuzz is often effective at quickly finding bugs, and developers are often quick to patch them. However, flaky bugs, timeouts, and out of memory errors are problematic, people rarely file CVEs for security vulnerabilities, and fuzzing campaigns often exhibit punctuated equilibria, where developers might be surprised by large spikes in bugs found. Our findings have implications on future fuzzing research and practice.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 4.
7 papers.
- 2025FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AIcited by 2
- 2023Reinforcement learning guided fuzz testing for a browser's HTML rendering enginecited by 1
- 2024Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Projectcited by 0
- 2026Fuzzing with Agents? Generators Are All You Needcited by 0
- 2026Agentic Method for Deterministic Validation of Legacy Code Migrationcited by 0
- 2026Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agendacited by 0
- 2026CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wildcited by 0
- 2026CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild24 Aug 2026
- 2026Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda21 Aug 2026
- 2026Agentic Method for Deterministic Validation of Legacy Code Migration30 Jul 2026
- 2026Fuzzing with Agents? Generators Are All You Need1 Apr 2026
- 2025FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI2 Oct 2025
- 2024Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Project26 May 2024
- 2023Reinforcement learning guided fuzz testing for a browser's HTML rendering engine27 Jul 2023