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OverrideFuzz: Semantic-Aware Grammar Fuzzing for Script-Runtime Vulnerabilities

arXiv Security Archived May 14, 2026 ✓ Full text saved

arXiv:2605.12563v1 Announce Type: new Abstract: Script-language runtimes such as Python, Lua, and JavaScript are widely deployed in security sensitive contexts, yet they remain difficult to test because valid inputs must satisfy syntax, dynamic type constraints, and object-level semantics. Existing grammar and reflection-based fuzzers improve syntactic validity and interface reachability, but they rarely model override hooks, dynamic rebinding, and attribute-resolution behavior that can redirect

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✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 12 May 2026] OverrideFuzz: Semantic-Aware Grammar Fuzzing for Script-Runtime Vulnerabilities Yiran Qiu Script-language runtimes such as Python, Lua, and JavaScript are widely deployed in security sensitive contexts, yet they remain difficult to test because valid inputs must satisfy syntax, dynamic type constraints, and object-level semantics. Existing grammar and reflection-based fuzzers improve syntactic validity and interface reachability, but they rarely model override hooks, dynamic rebinding, and attribute-resolution behavior that can redirect built-in operations across the script-native boundary and trigger use-after-free or type-confusion bugs. We present OverrideFuzz, a two-phase, semantic-aware grammar fuzzer for script-language runtimes. Its declaration phase constructs objects with overriding methods, while its execution phase generates operations that route through those hooks. Active reflection tracks runtime types, and passive reflection learns from error messages to remove invalid operation shapes, allowing generation to approach semantic correctness without manual API specification. We evaluate OverrideFuzz on CPython, Lua, and QuickJS. All three targets show consistent coverage growth, with rapid early expansion followed by slower incremental gains, and Lua benefits most from its pervasive metamethod dispatch mechanism. Although OverrideFuzz did not discover novel vulnerabilities during the bounded evaluation period, corpus analysis shows that it reconstructs inputs matching known vulnerability patterns, which suggests that semantic-aware generation reaches the intended script-native boundary behaviors. Comments: 37 pages, 7 figures, Bachelor Thesis, prepared with Typst Subjects: Cryptography and Security (cs.CR); Programming Languages (cs.PL) ACM classes: D.4.6 Cite as: arXiv:2605.12563 [cs.CR]   (or arXiv:2605.12563v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.12563 Focus to learn more Submission history From: Yiran Qiu [view email] [v1] Tue, 12 May 2026 03:57:35 UTC (946 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.PL 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 14, 2026
    Archived
    May 14, 2026
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