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MACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain security vulnerabilities. Secure code generation is inherently challenging because it requires solving a multi-objective problem: functional correctness and security. Existing approaches address this challenge by injecting external security knowledge or by using agentic feedback and iterative refinement. However, guideline retrieval often leaves the generator to translate generic advice into task-specific secure implementations, while shared-dialogue multi

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