AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities

Understand

Large Language Models (LLMs) have emerged as promising tools in software development, enabling automated code generation and analysis.

• However, their knowledge is limited to a fixed cutoff date, making them prone to generating code vulnerable to newly disclosed CVEs.
• Frequent fine-tuning with new CVE sets is costly, and existing LLM-based approaches focus on oversimplified CWE examples and require providing explicit bug locations to LLMs, limiting their ability to patch complex real-world vulnerabilities.
• To address these limitations, we propose AutoPatch, a multi-agent framework designed to patch vulnerable LLM-generated code, particularly those introduced after the LLMs' knowledge cutoff.