---
title: "Comment by Correlic on \"Secure-by-Design Patterns for LLM-Backend APIs\" | Hashnode"
description: "Really solid layered approach here. The defense-in-depth pipeline diagram is especially useful — too many teams treat prompt injection defense as a single-layer"
url: "https://hashnode.com/posts/secure-by-design-patterns-for-llm-backend-apis/69d76c88fa7251682e73447c/comment/69d98da0c8e5007ddb191fe1"
---

[Discussion](https://hashnode.com/posts/secure-by-design-patterns-for-llm-backend-apis/69d76c88fa7251682e73447c)

Comment [Secure-by-Design Patterns for LLM-Backend APIs](https://hashnode.com/posts/secure-by-design-patterns-for-llm-backend-apis/69d76c88fa7251682e73447c)

[Correlic](https://hashnode.com/@correlic)

[Apr 10](https://hashnode.com/posts/secure-by-design-patterns-for-llm-backend-apis/69d76c88fa7251682e73447c/comment/69d98da0c8e5007ddb191fe1)

Really solid layered approach here. The defense-in-depth pipeline diagram is especially useful — too many teams treat prompt injection defense as a single-layer problem (just the system prompt) and miss that you need independent controls at input screening, output validation, and tool privilege boundaries. One thing I'd add: the regex-based input screening is a good first pass, but in practice attackers are moving toward multi-turn injection and encoded payloads (base64, Unicode homoglyphs) that regex misses entirely. The LLM classifier fallback helps, but there's an interesting cost-security tradeoff there since you're now spending tokens on every request just for classification. The RAG source trust scoring is underrated — I've seen production systems where user-uploaded PDFs get the same retrieval weight as internal docs, which is essentially handing attackers a direct line into the context window. Labeling unverified sources in the prompt context is a simple but effective mitigation that more teams should adopt.