Large Language Models (LLMs) can generate functionally correct code that remains insecure. Security-focused fine-tuning can be expensive and is not always available, motivating inference-time approaches. Security-oriented prompting and retrieval can reduce vulnerabilities, but may also remove required behavior or apply defenses incompatible with the task. The challenge is to identify relevant security knowledge and use it to improve security without sacrificing functionality. We present SecGent, an inference-time framework that retrieves weakness context from the Common Weakness Enumeration (CWE) database and applies it to secure code generation. By connecting task specifications to relevant textual CWE records, SecGent provides task-specific security guidance without relying on retrieved vulnerable or patched code examples. Complementary functional and security tests check the resulting implementation, and their execution feedback guides repairs toward satisfying both objectives. We evaluate SecGent with four open-source LLMs and Claude Sonnet 5.5 across five programming languages on the CWEval benchmark. It achieves the highest joint functional--security success against four inference-time baselines for all five models. Across the open-weight models, mean functional-security@1 (FS@1) improves by 15.5% over RAG, with gains in both functionality and security, and by 19.8% over the strongest prompt optimizer compiled for each model. On Sonnet, FS@1 reaches 92.1%, outperforming the strongest baseline AutoGen by 4.8%. In a separate comparison, SecGent outperforms Claude Code with security-focused prompting by over 6% in FS@1. Feedback-guided repair further improves FS@1 by up to 6.7% over generation without feedback, primarily by recovering functionality while checking security.

翻译：暂无翻译