Large Language Models are increasingly used to generate code, but they introduce significant software risks. Because LLMs prioritize producing functional syntax rather than secure code, they often replicate insecure patterns learned from public codebases. They may apply coding practices inconsistently across languages, creating hidden vulnerabilities that give developers a false sense of safety. Security also depends heavily on how prompts are written; without explicit guidance, LLMs rarely incorporate proper safeguards. These factors make LLM‑generated code unreliable without thorough review and additional security measures.

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