RAG Security for AI Agents That Act on Retrieved Context
RAG connects LLMs and agents to enterprise knowledge. That improves usefulness, but it also introduces source trust, context poisoning, over-sharing, and auditability problems.
What is RAG security?
RAG security protects retrieval-augmented generation systems from poisoned documents, untrusted sources, vector or embedding weaknesses, over-broad retrieval, sensitive context leakage, and downstream actions based on manipulated context.
Search intent this page answers
RAG security queries usually ask how to trust retrieved context and govern what the AI system can do with it.
- What is RAG security?
- How do you prevent RAG poisoning?
- How do vector database risks affect LLM applications?
- How should retrieved context be audited?
Risk areas
RAG risk appears when retrieved content becomes instruction, authority, or privileged context.
| Risk | Why it matters | Rutile response |
|---|---|---|
| Poisoned documents | Injected or malicious content enters retrieval results and manipulates model behavior. | Source metadata and policy checks before tool execution. |
| Context over-sharing | The model receives more sensitive context than the task requires. | Data scope and permission boundaries. |
| Vector and embedding weakness | Similarity retrieval can surface misleading or unauthorized context. | Audit source, prompt hash, and action decisions. |
| Action on untrusted context | Agents use retrieved content to trigger real workflow changes. | Policy proxy and runtime enforcement. |
Rutile control model
Rutile focuses on what agents are allowed to do after retrieval happens.
| Control | Implementation pattern | Rutile capability |
|---|---|---|
| Source-aware policy | Treat retrieved context as data with provenance and risk, not as trusted instruction. | Policy context and audit evidence. |
| Least privilege retrieval | Limit data access by task, owner, agent, and resource. | JIT/JEA permissions. |
| Tool-call mediation | Do not let retrieved text directly authorize a tool action. | MCP/A2A Tool Proxy. |
| Evidence trail | Record which retrieved context influenced actions. | Audit log schema. |
Primary references
These references connect RAG security to LLM and AI risk frameworks.
OWASP Top 10 for Large Language Model Applications
Defines critical LLM application risks including prompt injection, sensitive information disclosure, excessive agency, and vector or embedding weaknesses.
NIST AI RMF Generative AI Profile
Applies NIST AI RMF concepts to generative AI risks and mitigation practices.
MITRE ATLAS
Documents adversary tactics and techniques against AI-enabled systems.
Related AI security topics
RAG Security FAQ
Is RAG security only vector database security?+
No. Vector stores matter, but RAG security also covers source trust, document ingestion, retrieval scope, context handling, downstream actions, and audit evidence.
How does Rutile help with RAG security?+
Rutile helps govern the agent action path after retrieval: permissions, tool calls, data scope, policy decisions, runtime monitoring, and evidence.
Rutile control model
Rutile focuses on what agents are allowed to do after retrieval happens.