AI Security · Global
You shipped an LLM, an agent, or an AI feature. Nobody has attacked it yet. We do, the way a real intruder would, and hand you the exploit paths with the guardrails to close them. Independent testing, mapped to the OWASP LLM Top 10 and the NIST AI Risk Management Framework.
CREST-certified operators · every finding human-verified · OWASP LLM Top 10 · NIST AI RMF
AI red teaming is adversarial security testing of an AI system by testers who attack it the way a real threat actor would. It tests the whole deployed system: the prompts, the retrieval pipeline, the tools and APIs the model can call, the permissions it holds, and the business logic around it. The goal is simple. Find the attack paths that create real risk, prove they work, and confirm they are closed after a fix.
A model evaluation checks the model in isolation. A penetration test checks the infrastructure around it. Only an AI red team tests the system an attacker actually meets, which is why the UK now names it as an expectation rather than a nice-to-have.
What we test
Your AI fails in ways traditional testing was never built to find. These are the four we start with.
We hide instructions in the content your AI reads, a document, a web page, a support ticket, and turn the model against its own rules. Direct and indirect. It is the most common serious weakness in LLM systems.
The injection paths, closed
We try to make your AI leak what it should not: training data, other users’ information, secrets in its context. If a prompt can pull it out, an attacker can too.
What leaks, and how to stop it
If your AI can call tools, browse, or take actions, we steer it into misusing them. An agent tricked into running the wrong command is a breach with no malware.
The actions an attacker can force
We test whether your outputs let someone reconstruct the model or its training data, the model-theft and inversion risks the frameworks name directly.
Your model, protected
Which test do you need?
Every assessment starts where an attacker would: outside, watching, looking for the one door left ajar. We find it, then we show you the walk-through.
| Activity | What it checks | What it misses |
|---|---|---|
| Penetration test | Infrastructure: servers, APIs, access controls | The model and its prompts |
| Model evaluation | The model in isolation: accuracy, bias, refusals | The tools, permissions and data it can reach |
| AI red team | The whole running system as an attacker meets it | Nothing by design. It connects the model to real business risk. |
The emerging standard
AI security testing is moving from optional to expected. The OWASP LLM Top 10 catalogues the failure modes, the NIST AI Risk Management Framework asks you to test for them, and ETSI TS 104 223 turned the same requirements into the first global baseline standard.
Before you ask
Every assessment starts where an attacker would: outside, watching, looking for the one door left ajar. We find it, then we show you the walk-through.
AI red teaming is adversarial security testing of an AI system by testers who attack it the way a real threat actor would. It tests the whole deployed system, the prompts, the retrieval pipeline, the tools and APIs the model can call, the permissions it holds, and the business logic around it, to find the attack paths that create real risk and prove they are closed after a fix. Common findings include prompt injection, data exfiltration through the model, tool misuse, and an agent taking actions it should not.
A penetration test checks the infrastructure around a model: servers, APIs, access controls. It says nothing about whether the model can be talked into ignoring its instructions. AI red teaming targets the AI-specific failure modes, prompt injection, data poisoning, model inversion, and tool abuse, that a standard pen test never looks for. If your AI reads untrusted content and can then act, you need both.
No single global law mandates it yet, but the expectation is hardening fast. The OWASP LLM Top 10 catalogues the failure modes, the NIST AI Risk Management Framework asks organisations to test for them, the EU AI Act requires adversarial testing for high-risk systems, and ETSI TS 104 223 set the first global baseline standard. Regulated sectors already carry operational-resilience duties that a live AI system falls under.
A CREST-certified team red-teams your specific AI system, your model, your prompts, your agents, your data, and hands you the exploit paths with the guardrails to close them. Every finding is verified by a human before it reaches you, and the report is written so your board and your framework evidence both hold. Start with a free audit.
Test your AI before an attacker does.
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