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Quality-Assured Fuzz Harness Generation via the Four Principles Framework

arXiv Security Archived May 22, 2026 ✓ Full text saved

arXiv:2605.21824v1 Announce Type: new Abstract: Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs that bridge fuzzers and library APIs. A growing body of tools now automate harness generation, but none systematically ensures the correctness of produced harnesses: logic errors, API misuse, and lifecycle violations go undetected at the source level. As LLM-driven generati

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    Computer Science > Cryptography and Security [Submitted on 20 May 2026] Quality-Assured Fuzz Harness Generation via the Four Principles Framework Ze Sheng, Dmitrijs Trizna, Luigino Camastra, Zhicheng Chen, Qingxiao Xu, Jeff Huang Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs that bridge fuzzers and library APIs. A growing body of tools now automate harness generation, but none systematically ensures the correctness of produced harnesses: logic errors, API misuse, and lifecycle violations go undetected at the source level. As LLM-driven generation scales harness creation, uncontrolled quality turns scale into a liability. We present QuartetFuzz, an autonomous harness-generation system that systematically improves correctness throughout the generation process. At its core is the Four Principles framework -- Logic Correctness (P1), API Protocol Compliance (P2), Security Boundary Respect (P3), and Entry Point Adequacy (P4) -- the first source-level definition of harness correctness with mathematical specifications and implementable checks. We operationalize these principles in an autonomous LLM agent that produces harnesses satisfying P1-P4 through a generate-check-fix loop before any fuzzing begins. Deployed on 23 open-source projects spanning C/C++, Java, and JavaScript, the system submits 42 bug reports, of which 29 are fixed or confirmed upstream (including 3 CVEs) and only 2 are rejected (4.8% FP rate). During generation, the built-in P1/P2 checks automatically intercepted 58 harness-induced crashes that would otherwise have been false positives. Applied as a quality auditor to 586 existing production harnesses across 70 projects, the system identifies 53 violations (45 confirmed, 35 fixed). We release a dataset of 100 labeled harnesses for reproducible evaluation. Code and dataset are available at this https URL Comments: 22 pages, 10 figures Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2605.21824 [cs.CR]   (or arXiv:2605.21824v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.21824 Focus to learn more Submission history From: Ze Sheng [view email] [v1] Wed, 20 May 2026 23:48:26 UTC (1,076 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    May 22, 2026
    Archived
    May 22, 2026
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