arXiv:2605.24300v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting strategies. Existing prompt engineering improves functional correctness but rarely ensures consistent security outcomes. We introduce the \textit{Mitigation-Aware Chain-of-Thought (MA-CoT)} framework, which embeds task-specific CWE mitigation guidance and language-aware safeguards to reduce recurring vulnerabilities in generated code. We evaluate MA-CoT across three LLMs (gpt-5, claude-4.5, gemini-
The rapid adoption of LLMs for code generation necessitates immediate solutions for security vulnerabilities, especially as their use scales across critical infrastructure.
Improving the security reliability of LLM-generated code is crucial for preventing widespread software supply chain attacks and maintaining trust in AI-assisted development.
With frameworks like MA-CoT, LLMs can now generate code with significantly reduced common vulnerabilities, shifting from merely functional correctness to inherent security.
- · Cybersecurity firms
- · Software developers
- · AI platform providers
- · Cyberattackers
- · Organizations with inadequate security practices
Increased enterprise adoption of LLMs for sensitive code generation tasks will follow from improved security guarantees.
The cost and time associated with security audits for AI-generated code will decrease, accelerating development cycles.
Heightened national security concerns surrounding software integrity could lead to regulatory frameworks mandating secure AI code generation practices.
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