SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization

arXiv:2604.03587v1 Announce Type: cross Abstract: Reasoning language models (RLMs) are increasingly used in programming. Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code. Prior training-based approaches for secure code generation face a critical limitation that prevents their direct application to RLMs: they rely on costly, manually curated security datasets covering only a limited set of vulnerabilities. At the inference level, generic sec