Large language models face various security threats including prompt injection, jailbreaking, and data exfiltration. A proxy-based defense architecture with policy engines can protect against these attacks by intercepting requests and responses before they reach the LLM. This approach uses AI models like LlamaGuard to detect malicious inputs, supports multiple LLMs with consistent security policies, and provides centralized logging. The defense-in-depth strategy blocks attacks like code injection, malicious URLs, and data leakage while maintaining system functionality.
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