Version 2025-09-22 By Spencer Brawner

Secure RAG: Architecture and Hardening

RAG security AI security Retrieval augmented generation Context security Source validation Data 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.

Published September 22, 2025

Section 01 // Key Facts

Key Facts

5 facts documented

RAG expands the attack surface through external or user-provided context.
[1]
Sanitization and source control mitigate injection and leakage.
[1]
Chunking and metadata help enforce context boundaries.
[1]
Retrieval logs enable incident analysis.
[2]
Output validation reduces unsafe actions.
[1]

Section 02 // Implementation

Implementation Steps

5 steps

01
Trust model for sources → signed sources, allow-lists.
02
Pre-processing filters → strip directives, PII filters.
03
Chunk & tag → chunk size policy, metadata schema.
04
Query filters & rate limits → rules, quotas.
05
Output validation & logging → validators, retrieval logs.

Section 03 // Glossary

Glossary

6 terms

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

Section 04 // References

References

2 sources

[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

Section 05 // Facts

Machine-Readable Facts

3 claims

[
  {
    "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"
  }
]