paperOctober 2025Unreviewed

FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI

Paschal C. Amusuo, Dongge Liu, Ricardo Andrés Calvo Méndez, Jonathan Metzman, Oliver Chang, James C. Davis

arXiv.org

Abstract

Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate random fuzzer inputs into valid arguments for the target function. Given the cost and expertise required to manually develop fuzz drivers, methods exist that leverage program analysis and Large Language Models to automatically generate these drivers. However,

Cite

@misc{amusuo2025falsecrashreducer,
  title = {{FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI}},
  author = {Paschal C. Amusuo and Dongge Liu and Ricardo Andrés Calvo Méndez and Jonathan Metzman and Oliver Chang and James C. Davis},
  year = {2025},
  month = oct,
  eprint = {2510.02185},
  archivePrefix = {arXiv},
  doi = {10.48550/arXiv.2510.02185},
  url = {https://www.semanticscholar.org/paper/3b3f1d457833d62e633eb67e411e7ae70a0b4058}
}