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P2 Efficient Greybox Fuzzing to Detect Memory Errors

高效灰盒模糊测试以检测内存错误
ASE Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering2022匹配度 1/10 · 该论文研究灰盒模糊测试与 sanitizer 开销优化，属于动态测试和内存安全方向，与用户关注的静态分析、LLM/Agent、浮点程序分析或贝叶斯程序分析均无直接交集，因此匹配度很低。引用 13 inbox
Jinsheng Ba, Gregory J. Duck, Abhik Roychoudhury
测试、调试与修复安全与隐私
crossref: 2026-09-12

AI 速览

一句话：研究如何降低 sanitizer 辅助灰盒模糊测试的额外开销，以更高效地检测内存错误。

灰盒模糊测试是检测现代软件安全漏洞与缺陷的有效方法，并可结合 AddressSanitizer（ASAN）等 sanitizer 来增强对缓冲区溢出、释放后使用等内存错误的检测。然而 sanitizer 会带来额外性能开销；摘要指出，使用 ASAN 进行模糊测试时开销约为 2.36 倍，部分抵消了其检测收益。近期研究将这类额外开销归因于程序启动与退出成本，这些成本在 fork 模式模糊测试中可能占主导。该论文面向高效灰盒模糊测试检测内存错误的问题，但给出的摘要未展开具体技术方法与实验评估结果。

问题
如何降低 sanitizer（如 ASAN）辅助灰盒模糊测试时引入的额外性能开销，从而更高效地检测内存错误。
方法
给出的摘要未说明具体方法；仅指出 sanitizer 的额外开销与 fork 模式下程序启动/退出成本可能占主导有关。
评估
给出的摘要未给出评估方式与结果，仅报告使用 ASAN 进行模糊测试时性能开销约为 2.36 倍。
原文摘要（Abstract）

Greybox fuzzing is a proven and effective testing method for the detection of security vulnerabilities and other bugs in modern software systems. Greybox fuzzing can also be used in combination with a sanitizer, such as AddressSanitizer (ASAN), to further enhance the detection of certain classes of bugs such as buffer overflow and use-after-free errors. However, sanitizers also introduce additional performance overheads, and this can degrade the performance of greybox mode fuzzing—measured in the order of 2.36 × for fuzzing with ASAN—partially negating the benefit of using a sanitizer in the first place. Recent research attributes the extra overhead to program startup/teardown costs that can dominate fork-mode fuzzing.

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BibTeX 
@inproceedings{paperbot7351,
  title = {Efficient Greybox Fuzzing to Detect Memory Errors},
  author = {Jinsheng Ba and Gregory J. Duck and Abhik Roychoudhury},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3561161}
}