How to Red Team an AI Agent: A Practical Guide

To red team an AI agent, you attack it on purpose with the techniques a real adversary would use, prompt injection, data exfiltration, jailbreaks, and tool abuse, then measure which attacks your controls stop and which get through. The goal is not a clean report. The goal is a list of the attacks that worked, so you can fix them before an attacker finds the same holes. Red teaming that never finds anything usually means the test was too weak.

This guide walks through the attack classes worth running, how to set up a test harness that will not hurt production, what to measure, and how to convert findings into controls that actually hold.

What does it mean to red team an AI agent?

Red teaming an AI agent is structured adversarial testing: you play the attacker against your own agent, in a controlled setting, to find where it can be hijacked or made to misbehave. It is different from evaluating an agent for quality. A quality eval asks whether the agent gives good answers. A red team asks whether the agent can be turned against you, which is a security question with a different set of tests.

It is also different from testing the model in isolation. The model can be perfectly aligned and the agent still exploitable, because the vulnerability usually lives in the application around the model: what the agent is allowed to read, which tools it can call, and whether anyone is watching. So you test the whole agent, with its real tools and data access, not just the prompt.

The attack classes to run

A good red team covers the categories that show up in real incidents. These map closely to the OWASP Top 10 for LLM Applications, which is a useful checklist to work against.

For inspiration on specific payloads, our writeup of prompt injection attack examples and the deep dive on indirect prompt injection give you concrete attacks to adapt.

Setting up a safe test harness

Never red team an agent that is wired to production data and live tools. A successful attack during testing should not be able to send a real email or delete a real record. Set up a harness with the same shape as production but safe blast radius.

1. Clone the agent's configuration, not its access. Use the same prompts, framework, and tool definitions, but point tools at sandboxed or mocked backends so a successful exfiltration lands somewhere harmless.
2. Instrument everything. You need to see every tool call the agent attempted, not just its final answer, because a blocked attack and a successful one can look identical from the outside.
3. Seed realistic content. If the agent reads documents or searches internal systems, include planted injection payloads in that content so you exercise the indirect path. Teams whose agents power internal search across every system should test against a realistic corpus, the kind an enterprise search tool would index, not three toy documents.
4. Automate the attack set. Keep your probes in a repeatable suite so you can rerun them after every change and catch regressions.

What to measure

The output of a red team is not "we tested it." It is a scored list of attacks with outcomes. For each probe, record whether the agent blocked it, held it for approval, or executed it, and treat every execution as a finding with a severity based on what the action could reach.

Severity should follow blast radius, not cleverness. An injection that only reads a public FAQ is low. The same injection that can call a payment tool or read customer data is critical, even if the payload was trivial.

Turning findings into controls

A red team that ends in a document changes nothing. The findings have to become enforced controls, and the controls have to stay on in production, not just during the test. Most successful attacks trace back to one of three missing controls.

• No input inspection. The agent acted on untrusted text without checking it. Fix: run injection detection on every input, including retrieved content.
• Too much access. A hijacked agent could reach tools and data far beyond its job. Fix: scope each agent to least-privilege permissions so a compromise is contained.
• No approval on irreversible actions. The agent completed a destructive action alone. Fix: put a human approval gate on actions you cannot undo.

The important move is that these become runtime controls, not test-time observations. It is common to harden an agent, pass the red team, and then ship without the firewall that made it pass. The attacks you defeated in testing come right back the moment the enforcement is off.

How often should you red team?

Red team before launch, after any change to the agent's tools, prompts, or data access, and on a regular cadence as new attack techniques appear. Prompt injection research moves fast, so a suite that was thorough six months ago has gaps today. Automating your probes makes the recurring runs cheap, which is the only way the regular cadence actually happens.

Red teaming with Agentshield

Agentshield includes AI red teaming tools that fire a maintained library of injection, exfiltration, jailbreak, and tool-poisoning attacks at your agent and report which ones your policy blocked, held, or missed. The same platform is the runtime firewall you keep on afterward, so the controls you hardened during the red team stay enforced in production. You attack, you fix, and the fix does not get left behind.

Read next: how to secure AI agents, or agentic AI security.