Our AI red teaming service probes your prompts, guardrails, and refusal behavior with adversarial inputs, then reports the attack patterns and how to fix them.
We probe in a test environment and report fixes. We do not exploit live systems or touch production data.
AI red teaming is structured adversarial testing of an AI system: trying to make it produce harmful, unsafe, or off-policy outputs before real attackers do. It covers jailbreaks, prompt injection, data exfiltration attempts, and guardrail bypasses. Good red teaming is methodical and documented, and it ends with prioritized fixes rather than a pile of scary screenshots. We run ours against your staging or test setup, never against live users. Our red teaming guide walks through the method in detail.
Most safety testing stops at the obvious cases. Attackers do not. Here is what we probe for.
Known and novel jailbreak techniques get tested against your system prompt and guardrails: roleplay frames, encoding tricks, multi-turn setups that erode refusals one step at a time. We document exactly which techniques worked and what the model said.
Systems that read untrusted content, like RAG pipelines and tool-using agents, can be steered by malicious text hidden in that content. We test whether your system follows the user's instructions or the attacker's.
Some systems refuse and then answer anyway. Others refuse harmless requests while waving through harmful ones. We map where your refusal behavior is inconsistent, because attackers live in those inconsistencies.
Not every harmful output breaks a written policy. Outputs that are technically allowed but would embarrass the company if screenshotted still count as failures in our grading. We flag them and suggest where the line should move.
You give us test-environment access and tell us what is in bounds. We agree the attack surface in writing.
Our team runs structured adversarial tests against your prompts and guardrails, and documents every successful attack.
Attack catalog with severity, reproduction steps, recommended fixes, and a walkthrough call.
Internal testing covers the cases your team thought of. Attackers specialize in the cases nobody thought of: the encoding trick, the multi-turn setup that erodes a refusal one polite message at a time, the hidden instruction in pasted content. Red teaming is how you meet those techniques before they meet your users.
Our probes are structured, not random. We work through attack families methodically and document what worked, what failed, and what almost worked, so your fixes address the technique and not just the one prompt we tried.
Users do not distinguish between the model and your guardrails; they experience one system. A refusal that leaks the answer anyway, a filter that blocks harmless requests while waving through harmful ones, these are product failures even when every component is working as designed.
Red teaming tests the assembled system the way the world will meet it. The findings map directly to product decisions: where the line sits, how refusals behave, and what monitoring should watch for after launch.
A red team report that ends at scary screenshots is entertainment, not engineering. Every finding we report comes with reproduction steps your team can verify, a severity grade so you fix the dangerous holes first, and concrete recommendations for prompts, guardrails, and monitoring.
The walkthrough call is where this pays off: we go through the attack catalog with your engineers, answer the how-would-you-fix-this questions, and leave you with a prioritized list instead of a vague sense of dread.
No. We work against a staging or test environment you provide, with the scope agreed in writing before we start. We do not touch production data, live users, or anything outside the agreed scope.
An attack catalog with reproduction steps for every successful attack, a severity grade on each finding, the patterns across them, and prioritized fix recommendations. Then a walkthrough call with your team.
Both, as configured. Most real failures sit at the boundary: the model is fine, the guardrails are fine, but the combination has a hole. We test the system as your users and attackers would meet it.
A focused pilot runs on the same rhythm as our evaluations: scoped in days, findings delivered promptly with a walkthrough. Larger attack surfaces get scoped after the pilot.
No. A pen test targets your infrastructure and code. AI red teaming targets model behavior: what the system can be convinced to say or do. The two complement each other, and we stay in our lane.
Yes. Findings stay inside the engagement, we work under NDA when you need one, and we never publish or reuse attack details from one client with another.
Jailbreaks, prompt injection, multi-turn manipulation, encoding tricks, guardrail bypasses, refusal inconsistencies, and data exfiltration attempts, among others. The list is illustrative, not exhaustive: we also test novel variants and combinations, because real attackers do not limit themselves to published techniques. The report documents everything we tried, not just what worked.
Tell us your attack surface and get a scoped red team plan back quickly.
Not sure where to start? Talk to us and we will point you at the right evaluation.