Prompt injection is an adversarial technique where an attacker crafts input that manipulates model behavior, often bypassing intended instructions. Understanding prompt injection is critical to protect your AI agents and services. Prompt injection can cause data exfiltration, policy violations, or dangerous outputs.
To defend against prompt injection, combine these practices: validate and sanitize user inputs, avoid concatenating raw user text into system prompts without controls, use role separation (system/tool/user), and incorporate deterministic refusal instructions. Test for prompt injection by creating adversarial test sets and include them in LLM evaluation.
Another defense against prompt injection is to use external verification: when the model is instructed to act on sensitive commands, require cryptographic or out-of-band verification. Logging all prompts and responses helps detect prompt injection attempts retroactively. Additionally, apply runtime AI Guardrails that inspect outputs for signs of injection and block suspicious responses.
Make prompt injection tests part of your CI and release process. Prioritize mitigations based on LLM evaluation results. Handling prompt injection at the prompt level and at runtime makes your AI more Reliable AI in production.
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