How to red-team your AI agents
Red-teaming asks the uncomfortable question: if someone tried to make this agent misbehave, would your guardrails hold? Here is how to test it and track the results.
Why red-team agents at all
You do not know whether a guardrail works until something pushes on it. Red-teaming an agent means deliberately trying to make it do things it should not - exceed its scope, be steered by injected content, exfiltrate data - and observing whether the controls catch it. The output is a list of gaps you can close before an attacker finds them.
- Prompt-injection attempts through content the agent reads.
- Tool misuse - coaxing calls outside the allowed set.
- Scope escape - reaching data or targets it should not.
- Excessive-agency probes - testing whether it can act beyond its task.
Running a red-team exercise
A repeatable exercise
- 1
Define attack vectors
List the ways an agent could be pushed to misbehave, mapped to your real risks.
- 2
Set expected outcomes
For each vector, state what the guardrail should do - block, gate, or flag.
- 3
Run and observe
Attempt each scenario and record what actually happened versus what should have.
- 4
Track and close
Log outcomes as evidence, prioritize by exploit likelihood and blast radius, and close the gaps.
How Fintra supports red-teaming
| Field | What it captures |
|---|---|
| Attack vector | The technique being tested. |
| Target agent | Which agent was probed. |
| Expected vs actual outcome | What the guardrail should have done and what it did. |
| Exploit likelihood | How feasible the attack is. |
| Blast radius | How far a success would reach. |
| Runtime evidence | The record tied to the run. |
Red-team checklist
- Attack vectors mapped to your real risks, not a generic list.
- An expected outcome defined for each scenario.
- Actual outcomes recorded against the expectation.
- Findings prioritized by exploit likelihood and blast radius.
- Gaps closed and re-tested.
- Results kept as evidence of a testing program.
Frequently asked questions
What is AI red-teaming?
AI red-teaming is deliberately attacking your own AI agents to find weaknesses - prompt injection, tool misuse, scope escape, excessive agency - before an adversary does. The goal is a prioritized list of gaps to close. Fintra provides a red-team results console that records runs, expected and actual outcomes, exploit likelihood, and blast radius.
Does Fintra automatically attack my agents?
No, and we are honest about that. Fintra is a red-team results console and evidence ledger with seeded example scenarios. You design and run the exercises; Fintra records the attack vector, target, expected versus actual outcome, exploit likelihood, blast radius, and evidence. It does not generate and execute exploits autonomously.
What should you test when red-teaming an agent?
The ways it could be made to misbehave: injection through content it reads, calling tools outside its allowed set, reaching data or targets it should not, and acting beyond its task. Define the expected guardrail behavior for each, then check whether reality matches. Fintra captures that expected-versus-actual comparison per run.
How do you prioritize red-team findings?
By how likely the exploit is and how far a success would reach. A feasible attack with a large blast radius is your top fix. Fintra records exploit likelihood and blast radius on each run so you can rank findings and close the most dangerous gaps first.
Stay in the loop
One practical finance briefing a week - new guides, checklists, and benchmarks.
Find the gaps before attackers do
Record red-team runs, outcomes, and blast radius as evidence. Free to start, no card required.
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