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RAG Security

Secure retrieval before it becomes agent context.

Focused RAG security resources for retrieval testing, prompt injection through retrieved content, and data leakage across indexes, summaries, citations, memory, and logs.

Separate document collections feed a permission-aware context selection path before a generated response.
Keep retrieval within authority.

Topic Pages

Focused pages for search and review.

Each page has its own URL, definition, scenario set, detection signals, testing guidance, and Orbyntis evaluation path.

RAG Security Testing

Test retrieval systems for permission leaks, poisoned context, unsafe citations, sensitive logs, and cross-role exposure.

Why it matters
Enterprise AI risk becomes concrete when a connected system can read data, invoke tools, influence approvals, or leave incomplete evidence.
How to test
  • 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.
Explore RAG Security Testing

RAG Prompt Injection

Evaluate how malicious retrieved content can influence model behavior, tool calls, approvals, and generated answers.

Why it matters
Enterprise AI risk becomes concrete when a connected system can read data, invoke tools, influence approvals, or leave incomplete evidence.
How to test
  • 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.
Explore RAG Prompt Injection

RAG Data Leakage

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

Why it matters
Enterprise AI risk becomes concrete when a connected system can read data, invoke tools, influence approvals, or leave incomplete evidence.
How to test
  • 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.
Explore RAG Data Leakage

RAG Security FAQ

Questions teams ask before production.

What is RAG security?

RAG security is the discipline of protecting retrieval-augmented generation systems across source data, indexing, retrieval, context assembly, model response, citations, logs, and downstream actions.

How does RAG data leakage happen?

RAG data leakage can happen when retrieval returns content outside a user's authorization, when summaries expose sensitive fragments, when citations reveal restricted sources, or when logs and memory preserve data that should not persist.

What is RAG prompt injection?

RAG prompt injection occurs when retrieved content contains instructions that try to alter the model's behavior, override policy, misuse tools, or change how the agent responds to the user.

How should RAG security testing be done?

Testing should combine authorization checks, adversarial documents, poisoned retrieval cases, sensitive data probes, citation review, log inspection, and end-to-end validation of how retrieved context affects tools or decisions.

Should retrieved content be treated as instructions?

No. Retrieved content should be treated as untrusted data unless the system has strong controls that separate source material from developer policy, authorization decisions, and tool execution rules.

How does Orbyntis evaluate RAG security?

Orbyntis evaluates RAG security by mapping data sources, access rules, retrieval behavior, prompt injection paths, leakage channels, citation integrity, evidence retention, and operational ownership.