Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking

[2608.14533] Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2608.14533 (cs) [Submitted on 14 Aug 2026] Title: Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking Authors: Ruizhe Wang , Meng Xu , N. Asokan View a PDF of the paper titled Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking, by Ruizhe Wang and Meng Xu and N. Asokan View PDF HTML (experimental) Abstract: Vulnerability detection via static analysis traditionally relies on security experts encoding insecure coding patterns into algorithmic rules. However, this approach often focuses on syntactic patterns and overlooks deeper semantic information in the code, such as the meanings of variable and function names. As software systems grow more complex, modeling vulnerabilities using only syntactic rules becomes increasingly challenging. In this paper, we propose a semantics-aware approach to detecting software vulnerabilities. We present SETYPE, a semantics-aware type system that can be derived directly from source code based solely on the meanings of symbols and expressions in natural language. In the SETYPE type system, both type inference and checking are performed by Large Language Models (LLMs), and a failed type check indicates a potential vulnerability. We prototype PYSETYPE to demonstrate the feasibility of SETYPE for detecting vulnerabilities in Python web applications. Our evaluation on real-world applications achieves 87% detection precision and 88% detection accuracy. Using PYSETYPE, we identified 15 potential zero-day vulnerabilities, nine of which were confirmed by developers. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.14533 [cs.CR] (or arXiv:2608.14533v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.14533 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ruizhe Wang [ view email ] [v1] Fri, 14 Aug 2026 17:49:19 UTC (264 KB) Full-text links: Access Paper: View a PDF of the paper titled Finding Vulnerabilities via LLM-Augmented Semantics-Aware Type-Checking, by Ruizhe Wang and Meng Xu and N. Asokan View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2026-08 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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