How to Detect and Block Prompt Injection in RAG Applications

Build layered defenses against prompt injection in RAG applications.

Attack Path

Attacker -> Malicious Content -> Knowledge Base -> Retriever -> LLM -> Tool/Data

Trust Boundaries

Separate system policy, user input, retrieved documents, tool results, and external content.

Retrieval Authorization

User -> Identity -> Document ACL -> Retriever -> Authorized Context

Detection

Use layered classifiers and rules for suspicious content, but do not rely on keyword matching as the only defense.

Tool Restriction

A summarization agent should not have destructive production tools.

Tool Authorization

LLM -> Tool Request -> Policy -> Risk Check -> Approval -> Execution

Testing

Build adversarial cases for direct injection, indirect injection, obfuscation, malicious PDFs/webpages, tool manipulation, and multi-turn attacks.

Metrics

Track injection attempts, successful attacks, block rate, unauthorized retrieval, unauthorized tool calls, and false positives.

Final Takeaway

Prompt injection requires defense in depth: secure retrieval, untrusted-content isolation, limited tools, external authorization, monitoring, and continuous testing.