How do you red-team an AI agent?
Red-teaming an agent means attacking it on purpose to find where it breaks. You feed it adversarial prompts, attempt prompt injection through the data it reads, try to push it past its permissions, and set up situations where the tempting action is the unsafe one. The goal is to confirm the agent refuses or contains the damage before a real attacker or a bad input finds the same gap.
What you are looking for
Regular testing checks that the agent does the right thing on normal inputs. Red-teaming checks that it does not do the wrong thing on hostile ones. Those are different questions, and an agent can pass the first while badly failing the second.
Attack angles worth trying
- Prompt injection: hide instructions in the content the agent reads, such as an email or webpage, and see whether it obeys
- Permission escalation: ask the agent to act outside its scope and check it refuses
- Destructive actions: set up a task where the fastest path is irreversible and confirm the agent hesitates
- Social engineering: phrase a harmful request as urgent or authoritative and see if the framing changes behavior
Tools and frameworks
Automated red-teaming tools include garak, PyRIT, and Promptfoo red teaming. Ground your threat model in the OWASP Top 10 for LLM Applications, which catalogs the common attack classes. And run all of it in a disposable, isolated environment, since red-teaming deliberately tries to trigger harmful actions that must never reach real systems.