CAT-RAG: Improving Secure Code Generation with Category-Aware Agentic Retrieval

Summary

CAT-RAG is an agentic retrieval-augmented code generation framework that uses an LLM categorizer agent to select compact, language-specific security categories in place of CWE-level retrieval. It works standalone or as a drop-in add-on to RACG pipelines without modifying the underlying model. On CyberSecEval it improves secure code generation by 23.57% versus 12.11% for the prior state of the art, while cutting token overhead by 41.71%.

Topics

• AI agents
• Retrieval-augmented generation