LLM-driven development comes with several security risks. Attackers can use prompt injection to bypass safeguards and manipulate models into producing insecure code with hidden vulnerabilities. If teams fail to validate LLM output, it can introduce exploits such as SQL injection or XSS into applications. Compromised or malicious training data may cause models to generate flawed code, while resource‑heavy queries can degrade model performance, disrupting development workflows. LLMs may also expose sensitive information like API keys, increasing the risk of credential leaks and insecure libraries entering the codebase.

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