AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing

Understand

Recent advancements in automatic code generation using large language models (LLMs) have brought us closer to fully automated secure software development.

• However, existing approaches often rely on a single agent for code generation, which struggles to produce secure, vulnerability-free code.
• Traditional program synthesis with LLMs has primarily focused on functional correctness, often neglecting critical dynamic security implications that happen during runtime.
• To address these challenges, we propose AutoSafeCoder, a multi-agent framework that leverages LLM-driven agents for code generation, vulnerability analysis, and security enhancement through continuous collaboration.