Attack Your Agents Before Someone Else Does
An AI red team probes the ways a non-deterministic agent can be manipulated into doing the wrong thing. Run the named vectors against your agents, then turn each finding into a control you can prove is enforced.
Why this is hard
Classic penetration testing probes networks and apps; it does not cover the ways an AI agent can be talked into an unsafe action. Because an agent responds differently to phrasing, the only honest way to know your guardrails hold is to attack them with the vectors an adversary would actually use - and then to close the gaps the attacks reveal.
- Agents fail in ways networks and apps do not - manipulation, not exploits
- The same guardrail can hold against one phrasing and fail against another
- A finding is only useful if it changes what the platform enforces
- You need repeatable cases so a fix can be re-verified
The approach, step by step
From adversarial test to enforced control
- 1
Pick the vectors
Choose the attack vectors that matter for your agents - prompt injection, tool misuse, privilege escalation, memory poisoning, data exfiltration, malicious MCP, insider attack, rogue agent, supply chain, unsafe autonomy.
- 2
Run the cases
Execute each vector against your agents from the red-team console, seeding demo runs first to see the console in action.
- 3
Read the results
For each finding, identify the guardrail that should have stopped it - a scope gap, a missing step-up, an over-scoped token.
- 4
Close the gap with policy
Change the policy so the guardrail holds, then re-run the case to confirm the action is now denied.
- 5
Keep the evidence
A blocked adversarial action, recorded to the ledger, is direct proof the control is enforced - retain it for security reviews.
How SentriAI does the work
SentriAI ships a red-team console with the named vectors, so you can attack your own agents and see how your governance holds. Each finding points at the guardrail that should have caught it, and because verdicts are deterministic, a fix you verify in the console is what will actually be enforced.
What you get out of the box
- A red-team console covering the named attack vectors
- Findings that point at the specific guardrail to fix
- Repeatable cases so a fix can be re-verified
- Blocked adversarial actions retained as control evidence
Avoid the common pitfall
Frequently asked questions
How do I run an AI red team?
Pick the attack vectors that matter - prompt injection, tool misuse, privilege escalation, memory poisoning, and more - run them against your agents from the red-team console, identify the guardrail each finding exposes, fix the policy, and re-run to confirm the action is now blocked.
What vectors should an AI red team cover?
At minimum prompt injection, tool misuse, privilege escalation, memory poisoning, data exfiltration, malicious MCP server, insider attack, rogue agent, supply chain compromise, and unsafe autonomous action - the manipulation paths specific to agentic systems.
How is AI red teaming different from a pentest?
A pentest probes networks and apps; AI red teaming probes the agent - the ways a non-deterministic actor can be manipulated into an unsafe action. Because agents respond to phrasing, you attack the guardrails directly.
Can red-team results be used as evidence?
Yes. A blocked adversarial action recorded to the tamper-evident ledger demonstrates a control is enforced in practice - exactly the artifact a security review or SOC 2 audit wants.
Stay in the loop
One practical finance briefing a week - new guides, checklists, and benchmarks.
Red-team your agents on a schedule
Run adversarial vectors and turn findings into enforced controls. Start free, no card required.
Talk to us