INTENTFIX: Automated Logic Vulnerability Repair via LLM-Driven Intent Modeling

Logic vulnerabilities, which arise from semantic gaps between a developer’s intent and the actual code, represent a critical and growing challenge in software security. Unlike syntactic bugs, these vulnerabilities pass traditional testing while harboring critical security flaws that can lead to severe breaches. We introduce INTENTFIX, a novel framework that automatically repairs logic vulnerabilities through intent-centric security analysis. INTENTFIX first leverages a Large Language Model (LLM) to systematically extract and formalize the developer’s implicit intent into a structured model. It then performs a differential analysis between this intent model and the implementation to precisely identify semantic gaps. Finally, it synthesizes and refines a patch through a multi-aspect, LLM-driven reasoning process.