What is Red Teaming (AI)?
SecurityDeliberately attacking your own AI system before someone else does: prompt injection attempts, approval-fatigue exploitation, boundary probes. For agents, red teaming tests the action path, not just the output.
Why It Matters
Red teaming is attacking your own system before someone else does, and for agents it has to test the action path, not just the output. A chatbot red team asks whether the model can be made to say something bad; an agent red team asks whether the agent can be made to do something bad: call the tool it should not, reach the system it should not, spend what it should not. The difference in target is the whole discipline, because an agent that outputs nothing harmful can still act harmfully through its tools.
What Agent Red Teaming Probes
Injection paths. Can instructions smuggled through documents, tool outputs, or input produce an unauthorized action? The test is whether the pre-action gate denies the injected call, not whether the model refused the wording.
Approval exploitation. Can the oversight layer be farmed? Rapid-fire requests, minimized language, risky actions batched inside benign ones: the approval-fatigue attack patterns run against your own reviewers.
Boundary probes. What can the agent reach that the design did not intend: credentials broader than the task, channels that double as escape paths, tools that expose more than their documented surface.
Where It Breaks
Red teaming fails as theater when the exercise tests the model and stops, because the model is the soft part and the runtime is where the real findings live. It also fails as a one-time exercise: the agent’s tools, credentials, and surface change continuously, so a red team result has a shelf life measured in releases. The useful version is a standing practice wired to changes in the attack surface, not a report commissioned once.
How Flytebit Handles It
Red teaming in our governance work runs against the action path: injected instructions aimed at producing unauthorized tool calls, fatigue exploitation against the escalation design, and boundary probes against the credential and containment layers. The findings land as policy-as-code changes, so each attack that succeeds becomes a control that prevents it. The engagement is our AI governance and risk work.