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.
For IT providers and consultancies
Your clients are shipping agents and copilots faster than anyone is testing them. You can answer that question yourself. We deliver the AI red team under your brand, on your paper, and your client never meets us. Wholesale terms sit about 20% below market rates, so the margin stays with you.
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?
| 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
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.
An AI red team tester attacks an AI system the way a real adversary would. They write prompts and plant content designed to make the model ignore its instructions, leak data or misuse its tools, chain small weaknesses into a working attack, and record each step so it can be reproduced. A good tester also proves the fix: they rerun the same attack after the change and confirm it no longer works.
The OWASP Top 10 for LLM Applications lists the failure modes a red team tests for: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption. We test each one that applies to your system and report every finding against its OWASP category, so your team and your auditors read the same map.
We use automated tooling, including AI-assisted payload generation, to cover the known attack patterns fast. Then a CREST-certified operator builds the attacks that matter for your system. Tools find the known patterns. The serious findings usually come from chaining your specific tools, data and permissions, which takes a person.
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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