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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, with external validation on LLMSecEval. Using static analysis with expert validation, MA-CoT reduces total security findings from 92 to 39 (57.6\\%) on the primary dataset and from 73 to 4 (94.5\\%) on LLMSecEval. 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Using static analysis with expert validation, MA-CoT reduces total security findings from 92 to 39 (57.6\\%) on the primary dataset and from 73 to 4 (94.5\\%) on LLMSecEval. 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