Enhancing Reliability in LLM-Based Secure Code Generation
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-2.5), three programming languages (C, Java, Python), and four prompting strategies (Vanilla, Zero-shot, CoT, MA-CoT) on a 200-task primary dataset,...
| Authors | Mohammed F. Kharma, Mohammad Alkhanafseh, Ahmed Sabbah et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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