AI RED TEAMING
AI Red Teaming & Adversarial Testing
Structured adversarial testing of your AI systems to find how they fail before an attacker, a customer, or a regulator does. We test your own models and the third-party ones you deploy, then feed every finding back into your controls.
Jailbreaks, prompt injection, data and model exfiltration, unsafe or biased outputs, and tool or agent misuse. Built for CISOs, Heads of AI, security leads, and Heads of Risk.
THE REALITY
AI Adds an Attack Surface Your Normal Testing Never Sees
A model can pass every conventional security check and still be jailbroken with a paragraph of text, leak its system prompt, or be talked into an unsafe action. Penetration tests and standard benchmarks look at the code and the infrastructure. They don't test the model's behaviour, and that's exactly where deployed AI breaks.
What Normal Testing Misses
- Jailbreaks that make the model ignore its own safety rules
- Prompt injection hidden in documents and web content the model reads
- System prompts, secrets, and user data pulled out through extraction
- Agents steered into unsafe actions against the systems they connect to
- Biased or non-compliant outputs the benchmark score never surfaced
What Adversarial Testing Finds
- Reproducible attacks ranked by real business impact
- The failure modes an attacker, a customer, or a regulator would hit
- Weaknesses in the vendor models you deploy, not just your own
- Findings mapped to OWASP, NIST AI RMF, and EU AI Act obligations
- Fixes fed back into controls, then re-tested to confirm they hold
OUR APPROACH
Scope, Attack, Report and Remediate
We work in three phases. Scope the attack surface and agree what a real failure looks like. Attack the system the way a motivated adversary would. Then report the findings and feed the fixes back into your controls, confirming each one holds.
Scope the Attack Surface
We map every AI system in play: your own models, the vendor models you deploy, the prompts, the tools and data they can reach, and the agents that act on their output. We agree the targets, the rules of engagement, and what a real failure looks like for your business before a single test runs.
Attack the System
We run structured adversarial testing against those targets. Jailbreaks, prompt injection, data and model exfiltration, unsafe or biased outputs, and tool or agent misuse. We test the way a motivated attacker would, then push further into the edge cases a customer or a regulator might stumble into by accident.
Report and Remediate
You get a ranked findings report with reproducible attacks, business impact, and fixes. Then we feed each finding back into your controls: input and output filters, system prompts, guardrails, monitoring, and human review. Fix the failure, then confirm the fix holds under the same attack.
WHAT WE DO
Five Ways We Try to Break Your AI
We test across five connected areas, working from the OWASP Top 10 for LLM Applications and aligning to the NIST AI Risk Management Framework, whose generative AI profile calls for adversarial testing. Every finding maps to a control you can fix.
Jailbreak and Prompt-Injection Testing
We try to make your models ignore their own rules. Direct jailbreaks, indirect prompt injection through documents and web content the model reads, and the layered attacks that combine both. This is the top category in the OWASP Top 10 for LLM Applications, and it's where most deployed systems break first.
Data and Model Exfiltration
We test whether an attacker can pull out what should stay in: training data, system prompts, other users' inputs, secrets held in context, or the model's own behaviour under extraction attempts. Sensitive information disclosure sits high on the OWASP list because the cost of getting it wrong is a breach you have to report.
Agent and Tool Abuse
When your AI can call tools, browse, or act on other systems, the blast radius grows. We test whether an agent can be steered into unsafe actions, escalate its own permissions, or be turned against the systems it connects to. Autonomous and agentic AI is exactly where our executive risk framework focuses.
Bias and Unsafe-Output Probing
We probe for outputs that are biased, harmful, or non-compliant, across the groups and edge cases your own testing tends to skip. For regulated deployments, this is where a wrong answer stops being a bug and becomes a legal or reputational problem.
Vendor and Third-Party Model Testing
You're accountable for the AI you deploy even when you didn't build it. We test the third-party and vendor models in your stack under your real configuration and data, so a supplier's weakness doesn't become your incident. Findings feed straight into your AI governance and vendor assessment.
We apply this in sectors where a failed AI system carries real cost. See how it lands for B2B SaaS and financial services.
Findings feed straight into your AI governance programme, so a weakness we find becomes a control you own. Check where you stand against high-risk obligations with our free EU AI Act readiness tool.
WHY US
Responsible AI Practitioners Who Attack to Defend
We red-team because we build and govern AI, not the other way round. We know how these systems fail because we know how they're meant to work, and we test them against the frameworks that regulators and buyers already use. The thinking behind this work is set out in three books by our founder.
PRIMARY
Ethical AI
Frameworks that turn responsible AI from an abstract principle into controls business leaders can actually operate, including how to test what you deploy.
ON TRANSFORMATION
TRANSFORM
A practical framework for taking an organisation through AI adoption without the change failing under its own weight.
ON ADVANTAGE
AI Moats
How to build an AI advantage competitors can't copy, including the trust and governed processes that a red-teamed system earns.
Go deeper on the thinking: the executive framework for AI agent risk assessment.
More across the red teaming knowledge base.
QUESTIONS
What Security and Risk Leaders Ask Before They Start
What is AI red teaming?
AI red teaming is structured adversarial testing of your AI systems to find how they fail before an attacker, a customer, or a regulator does. We act as a motivated adversary against your models and the ways they're deployed: we try to jailbreak them, inject hostile instructions, extract data they should protect, trigger unsafe or biased outputs, and misuse any tools or agents they can reach. Then we feed every finding back into your controls so the same attack doesn't work twice.
How is it different from a penetration test?
A penetration test targets your infrastructure: networks, servers, applications, and the classic vulnerabilities in them. AI red teaming targets the AI layer, which normal testing and standard benchmarks miss. A model can pass every conventional security check and still be jailbroken with a paragraph of text, leak its system prompt, or be talked into an unsafe action. The attack surface is the model's behaviour, not just the code around it, so it needs a different discipline. The two are complementary, and serious deployments need both.
What do you test for?
Jailbreaks and prompt injection, data and model exfiltration, agent and tool abuse, and biased or unsafe outputs, across your own models and the vendor models you deploy. We work from the OWASP Top 10 for LLM Applications and align testing to the NIST AI Risk Management Framework, whose generative AI profile calls for adversarial testing. For high-risk systems under the EU AI Act, which expects testing and risk management, we map findings to the obligations you have to meet.
How often should we red-team?
Red-team before you ship a new AI system or a major change, and on a recurring basis after that. Models get updated, your prompts and tools change, and new attack techniques appear constantly, so a one-off test goes stale fast. For high-risk or regulated deployments, treat it as an ongoing part of your risk management rather than a single gate. We help you set a cadence that matches how fast your systems and your risk actually change.
Do you test third-party or vendor models?
Yes. You're accountable for the AI you deploy even when a supplier built it, so we test both your own models and the third-party ones in your stack. We test them under your real configuration, your data, and your integration, because a vendor model that's safe in a demo can still fail inside your deployment. Findings feed straight into your vendor assessment and your governance, so a supplier's weakness doesn't quietly become your incident.
RED-TEAM YOUR AI
Find How Your AI Fails Before Someone Else Does
Check where you stand against high-risk obligations with our free EU AI Act readiness tool, or speak with us about an adversarial testing programme across your own models and the vendor ones you deploy.
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