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Computer Science > Cryptography and Security

arXiv:2409.04504 (cs)
[Submitted on 6 Sep 2024]

Title:Comment on Revisiting Neural Program Smoothing for Fuzzing

Authors:Dongdong She, Kexin Pei, Junfeng Yang, Baishakhi Ray, Suman Jana
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Abstract:MLFuzz, a work accepted at ACM FSE 2023, revisits the performance of a machine learning-based fuzzer, NEUZZ. We demonstrate that its main conclusion is entirely wrong due to several fatal bugs in the implementation and wrong evaluation setups, including an initialization bug in persistent mode, a program crash, an error in training dataset collection, and a mistake in fuzzing result collection. Additionally, MLFuzz uses noisy training datasets without sufficient data cleaning and preprocessing, which contributes to a drastic performance drop in NEUZZ. We address these issues and provide a corrected implementation and evaluation setup, showing that NEUZZ consistently performs well over AFL on the FuzzBench dataset. Finally, we reflect on the evaluation methods used in MLFuzz and offer practical advice on fair and scientific fuzzing evaluations.
Comments: Comment on https://doi.org/10.1145/3611643.3616308
Subjects: Cryptography and Security (cs.CR)
Cite as: arXiv:2409.04504 [cs.CR]
  (or arXiv:2409.04504v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2409.04504

Submission history

From: Dongdong She [view email]
[v1] Fri, 6 Sep 2024 16:07:22 UTC (471 KB)
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