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Red Teaming

AI Agent Red Teaming

Red team autonomous AI workflows from instruction and context through identity, tool use, approval, and evidence. The goal is to make the control boundary visible before the agent operates in production.

Adversarial context and tool state probes reach different stages of an agent workflow.
Challenge the path to business action.

Definition

What is Agent Red Teaming?

Red team autonomous AI workflows from instruction and context through identity, tool use, approval, and evidence.

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 agent red teaming 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 agent red teaming.

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.