StateLens：基于 LLM 引导插桩的 JavaScript 引擎状态感知模糊测试

Computer Science > Cryptography and Security arXiv:2609.24550 (cs) [Submitted on 21 Sep 2026] Title:State-Aware Fuzzing of JavaScript Engines with LLM-Guided Instrumentation Authors:Wai Kin Wong, Dongwei Xiao, Anthony Cheuk Tung Lai, Ping Fan Ke, Shuai Wang View a PDF of the paper titled State-Aware Fuzzing of JavaScript Engines with LLM-Guided Instrumentation, by Wai Kin Wong and 4 other authors View PDF HTML (experimental) Abstract:The security of the modern web depends on the correctness of JavaScript (JS) engines, yet these complex systems remain vulnerable to high-impact bugs. A critical limitation of state-of-the-art fuzzers is the coverage plateau: once a fuzzer saturates the control-flow graph, edge coverage loses its ability to guide discovery. Because complex engine behaviors, such as JIT optimization tiers and hidden class transitions, often share identical edge coverage, standard coverage metrics are blind to the distinct internal states required to trigger deep errors. To bridge this gap, we present StateLens, a framework that employs Large Language Models (LLM) to automate the discovery of deep internal states. Blindly placing instrumentation probes at all states is infeasible due to the vast state space and the high runtime overhead. StateLens introduces a novel agent-based reasoning pipeline that emulates the intuition of a security researcher. By iteratively traversing code and developer comments, our agents intelligently select high-value instrumentation targets, effectively separating logic-driving state variables from irrelevant data. This results in synthesizable, high-signal feedback probes that map the engine's hidden configurations. This instrumentation feeds a dual-feedback mechanism, effectively guiding the fuzzer toward unexplored engine semantics. Our evaluation confirms that StateLens significantly outperforms state-of-the-art fuzzers and uncovering 68 new bugs. Comments: Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2609.24550 [cs.CR] (or arXiv:2609.24550v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.24550 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3830418.3843900 Focus to learn more DOI(s) linking to related resources Submission history From: Dongwei Xiao [view email] [v1] Mon, 21 Sep 2026 13:16:21 UTC (195 KB) Full-text links: Access Paper: View a PDF of the paper titled State-Aware Fuzzing of JavaScript Engines with LLM-Guided Instrumentation, by Wai Kin Wong and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)