I recently built and shipped an AI-powered web app and quickly realized that traditional web security isn't enough when you're making API calls to LLMs. Every request costs money, users can manipulate model parameters, and prompt injection is a real threat.

I wrote up everything I implemented and learned:

Rate Limiting — Different limits for different endpoints. Chat endpoints need stricter limits because every call costs money.

Prompt Injection Detection — Pattern-based detection for common attack vectors (e.g., "ignore previous instructions"). Not foolproof, but an important layer.

Server-Side Parameter Validation — The big one. My app originally had max_tokens set to 100,000 on the client side. Anyone could have modified that. Now everything is validated and capped server-side.

Authentication — Moved from client-side password checking (yes, I know) to server-side auth with HTTP-only cookies, session tokens, and brute force protection.

Security Headers & CSP — The usual suspects, but with AI-specific considerations like restricting connect-src to only your LLM API provider.

Full article with code examples: https://medium.com/@jabrsalm449/securing-ai-powered-applications-a-comprehensive-guide-to-protecting-your-llm-integrated-web-app-dcf8d7963e78

Happy to answer questions about any of the implementations. What security challenges have you run into with AI integrations?