How do I protect my GenAI application from prompt injection attacks?

All answers to this question.

Securing an LLM requires a multi-layered defense. First, use "System Message" separation—clearly define the boundaries in the system prompt. Second, implement an input filter (like NeMo Guardrails or Llama Guard) to detect malicious patterns before they hit your model. Third, never let the LLM execute code or access APIs without a "Human-in-the-loop" or a strict sandbox. Finally, use a secondary "Supervisor" LLM that is much smaller and cheaper to scan the primary model's output for sensitive data or policy violations before the text is displayed to the final user.

   Answered 2024-03-24 by Patricia Collins

Have you tried "delimiters" in your prompts? Using things like triple quotes or XML tags can help the model distinguish between your instructions and the user's potentially malicious input.

   Answered 2024-03-25 by Steven Harris

• Steven, I started using XML tags like after your suggestion, and it has helped the model realize when a user is trying to "override" instructions. However, I’ve found that advanced attackers can still use "Base64 encoding" to bypass simple text filters. I’m now looking into an adversarial testing tool to "red team" our prompt before we go live. It’s a cat-and-mouse game, but the XML tagging definitely added a needed layer of structural clarity.

     Commented 2024-03-27 by Amanda White

Check out the OWASP Top 10 for LLMs. It covers things like "Insecure Output Handling" which is just as dangerous as the input injection itself.

   Answered 2024-03-28 by James Wilson

• Great resource, James! I just read through the OWASP list and it’s a wake-up call for any developer. We're adding output validation to our roadmap immediately.

     Commented 2024-03-29 by Patricia Collins