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Data Boundary

RAG Data Leakage

Control how retrieved content, citations, summaries, memory, and logs expose sensitive information. The goal is to make the control boundary visible before the agent operates in production.

Sensitive retrieved content can spread through answers, citations, and retained logs.
Review every output surface.

Definition

What is RAG Data Leakage?

Control how retrieved content, citations, summaries, memory, and logs expose sensitive information.

Enterprise AI risk becomes concrete when a connected system can read data, invoke tools, influence approvals, or leave incomplete evidence.

Attack Scenarios

Where this shows up in real agent workflows.

The exact exposure depends on authority, connected systems, identity, approval state, and evidence quality.

01

Scenario

A production workflow relies on rag data leakage assumptions that were only tested in a pilot.

02

Scenario

A connector, retriever, or tool response changes agent behavior without a clear owner.

03

Scenario

Security and governance teams cannot see where the control is enforced.

Business Impact

Why it matters

  • Unmapped authority can expand the blast radius of a single agent failure.
  • Sensitive data or business actions can move outside intended controls.
  • Approval and audit teams may lack enough evidence to trust the workflow.

Exploit Path

How attackers exploit it

  • Find a weak boundary between untrusted input, retrieved context, identity, and tool authority.
  • Steer the agent toward a capability that is available but not appropriate for the current workflow.
  • Use missing validation or missing evidence to make the unsafe action look routine.

Detection and Testing

How to detect and test for rag data leakage.

Detection signals

  • Broad scopes, vague tool descriptions, or unclear server ownership.
  • Tool calls or retrieval results that do not match requester authority.
  • Logs that omit context source, policy result, approval state, or owner.

Test methods

  • Map the workflow from request to business effect.
  • Test adversarial prompts, retrieved content, tool parameters, and approval states.
  • Verify controls outside the model and preserve enough evidence for review.