A structured JSON intermediate representation for LLM-generated static analysis queries outperforms both direct generation and agentic tool use, with gains of 15-25 percentage points on large models.
QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration.CoRRabs/2506.23644 (2025)
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Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.
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Less Is More: Measuring How LLM Involvement affects Chatbot Accuracy in Static Analysis
A structured JSON intermediate representation for LLM-generated static analysis queries outperforms both direct generation and agentic tool use, with gains of 15-25 percentage points on large models.
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A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection
Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.