MA-CoT: Mitigation-Aware Chain-of-Thought Reduces LLM Code Vulnerabilities by 57.6% Across GPT-5, Claude 4.5, Gemini 2.5

arXiv·high signal

Introduces the Mitigation-Aware Chain-of-Thought framework embedding CWE mitigation guidance and language-aware safeguards into prompting. Evaluated across GPT-5, Claude 4.5, and Gemini 2.5 on C/Java/Python with 200 tasks: reduces security findings from 92 to 39 (57.6%) on primary dataset and from 73 to 4 (94.5%) on LLMSecEval. A practical, model-agnostic technique any developer can adopt immediately for secure code generation.