Answer CardVersion 2025-09-22September 22, 2025

Secure RAG: Architecture and Hardening

RAG securityAI securityRetrieval augmented generationContext securitySource validationData sanitization

TL;DR

RAG blends model reasoning with retrieved context. Risks arise when retrieved content carries hidden instructions, sensitive data, or untrusted links. Harden by sanitizing inputs, signing/whitelisting sources, chunking and metadata controls, query filtering, and output validation. Log retrievals for forensics.

Key Facts

RAG expands the attack surface through external or user-provided context.
[1]Source: NIST AI Risk Management Framework
Sanitization and source control mitigate injection and leakage.
[1]Source: NIST AI Risk Management Framework
Chunking and metadata help enforce context boundaries.
[1]Source: NIST AI Risk Management Framework
Retrieval logs enable incident analysis.
[2]Source: ISO 42001 AI Management Systems Standard
Output validation reduces unsafe actions.
[1]Source: NIST AI Risk Management Framework

Implementation Steps

Trust model for sources → signed sources, allow-lists.

Pre-processing filters → strip directives, PII filters.

Chunk & tag → chunk size policy, metadata schema.

Query filters & rate limits → rules, quotas.

Output validation & logging → validators, retrieval logs.

Glossary

RAG: Retrieval-Augmented Generation - AI technique combining retrieval and generation
Chunking: Process of breaking documents into manageable pieces for retrieval
Metadata: Descriptive information about data sources and content
Signed source: Data source with cryptographic verification of authenticity
Injection: Attack where malicious content influences AI system behavior
Retrieval log: Record of what content was retrieved and used in AI responses

References

[1] NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework
[2] ISO 42001 AI Management Systems Standard https://www.iso.org/standard/78380.html

Machine-readable Facts

[
  {
    "id": "f-surface",
    "claim": "RAG increases attack surface through retrieved or user-provided context.",
    "source": "https://www.nist.gov/itl/ai-risk-management-framework"
  },
  {
    "id": "f-sanitize",
    "claim": "Sanitization and source allow-lists mitigate prompt injection via context.",
    "source": "https://www.nist.gov/itl/ai-risk-management-framework"
  },
  {
    "id": "f-logs",
    "claim": "Retrieval logging supports incident investigation and assurance.",
    "source": "https://www.iso.org/standard/78380.html"
  }
]

About the Author

Spencer Brawner

https://www.linkedin.com/in/jsbrawner