prompt-injection-guard

Prompt injection is an adversarial attack where malicious inputs manipulate an LLM's behavior. It compromises LLM security by causing unauthorized actions, jailbreaks, or the leaking of sensitive system instructions and data.

Prevent prompt injection in AI chatbots by applying multi-layered security strategies. This includes sanitizing user inputs via blocklists, structuring prompts defensively, embedding canary tokens, and using LLM-based detection before processing queries.

Input validation alone does not stop indirect prompt injection. Comprehensive defense requires output validation to verify LLM responses, canary tokens to detect instruction leaking, and LLM-based detection to catch layered adversarial inputs.

The best way to secure AI applications against jailbreaking and prompt leaking is implementing a multi-layered defense. This combines input validation, prompt structure defense, output validation, canary tokens, and LLM-based detection.

Yes, you can use canary tokens to detect prompt leaking. Embedding these markers in system prompts allows you to scan LLM outputs, verifying that sensitive instructions are not inadvertently exposed to users.

Output validation fails when adversarial inputs bypass basic blocklists. Upgrade to multi-layered defense combining prompt structuring, canary token tracking, and LLM-based detection to identify complex, direct, and indirect injection vectors.