Prompt Injection Defense for LLM Applications

Prompt injection happens when user-controlled or retrieved text tries to manipulate the model into ignoring instructions, leaking data, or misusing tools.

It is one of the core security issues in LLM applications.

Do not trust model output

The model can recommend an action, but your application must authorize it.

Enforce permissions outside the model

Never ask the model whether a user can access data. Check permissions before retrieval and before tool execution.

Validate tools

Tool calls need schema validation, authorization, rate limits, and audit logs.

Separate instructions

Keep system instructions, user input, retrieved content, and tool results clearly separated.

Final thoughts

Prompt injection defense is not one filter. It is a layered architecture: permissions, validation, least privilege, logging, and cautious tool design.