Adversarial testing
We attack your AI systems the way a motivated user or an actual attacker would — then hand you reproducible findings, ranked by what they would really cost you, with the fixes that close them.
Why red team AI
A penetration test looks for flaws in code and infrastructure. An AI system fails differently: the input is natural language, the boundary between instruction and data is blurred by design, and the model will do what it is convincingly asked to do. None of that shows up in a scanner.
Agentic systems raise the stakes again. Once a model can call tools, write to systems and act without a human in the loop, a successful prompt injection stops being an embarrassing screenshot and becomes an action taken against your business, with your credentials, by your own software.
Red teaming is how you find that out on your own terms, before someone else does it on theirs.
Attack surface
Scope is set with you, but these are the classes of failure we work through on a typical engagement. Each maps to the OWASP Top 10 for LLM Applications and the relevant MITRE ATLAS techniques.
Overriding system instructions, extracting the system prompt, and getting the model to abandon its guardrails through framing, role play, encoding and multi-turn pressure.
Instructions hidden in the content the system ingests — a web page, an email, a PDF, a calendar invite, a support ticket — that the model treats as though you had typed them.
Whether an agent can be talked into calling tools it should not, with arguments it should not, and what the blast radius is when it does. Usually where the real findings are.
Getting the system to reveal other tenants' data, documents outside the user's entitlement, credentials in context, or content from the retrieval index it should never surface.
Poisoning the index, crossing document-level permissions through retrieval, and inferring restricted content from embeddings and similarity behaviour.
Whether the model reproduces material from fine-tuning data, and whether anything sensitive made it into a training set it should not have.
Model output flowing unsanitised into a browser, a shell, a database or a downstream API — where a language problem becomes a conventional XSS, SSRF or injection vulnerability.
What it takes to make the system produce content that is unsafe, discriminatory, legally problematic or simply catastrophic in a screenshot — and how reliably.
Where the system invents facts, misattributes citations or states things with unearned confidence, and which prompts reliably push it there.
Inputs that drive cost or latency through the roof, loops an agent cannot exit, and whether anyone would notice the bill before month end.
Provenance of the models, adapters, embeddings, MCP servers and third-party tools your system trusts, and what each of them could do if compromised.
Testing the filters, classifiers and moderation layers themselves — how they fail, how to get around them, and whether anything is logged when someone tries.
Method
Authorised, scoped and documented from the first day. Nothing is tested without written permission.
Systems in scope, rules of engagement, data handling, escalation contacts and a signed authority to test.
Who would attack this, what they would want, and which failures would actually hurt. Testing follows the threat model, not a checklist.
Manual adversarial work alongside automated probing at scale, because the interesting findings come from a human following a hunch.
Individual weaknesses combined into a realistic attack path, then reduced to a reproducible case with a measured success rate.
Findings, fixes and an executive summary — then a retest once you have remediated, so you can evidence closure.
Standards
Every finding is mapped to recognised frameworks, so the report works as evidence in a compliance conversation rather than only as a technical to-do list.
The common reference for LLM-specific risk. Every finding carries its OWASP category.
Adversary tactics and techniques for AI systems, used to describe attack paths in language security teams already know.
Results feed the Measure function, including the Generative AI profile, as evidence of ongoing risk measurement.
Supports the accuracy, robustness and cybersecurity duties for high-risk systems, and adversarial testing expectations for general-purpose models.
Red teaming measures where you are; it does not govern what you do next. Where an engagement surfaces structural gaps — no owner, no policy, no control set — that is the province of our AI Governance practice, and we will say so rather than sell you another test.
Deliverables
A report your engineers can act on without a translation layer, and a summary your board can read without one either.
Every finding includes the exact prompts and conditions to reproduce it, how often it worked, and what we would change to close it.
Each issue with severity, reproduction steps, success rate, affected component and framework mapping.
The chained paths that matter, written as a story an executive can follow from entry point to impact.
Specific fixes at the right layer — prompt, architecture, tool permissions, filtering or monitoring.
The successful attacks packaged as tests you can run in CI, so a future change cannot quietly reopen them.
The position, the exposure and the decisions required, in plain language and without jargon.
Verification once fixes land, and a statement of what was retested and what now passes.
Experience
We have run red team engagements against production AI systems for two client organisations to date. Engagement details are confidential; we are happy to talk through the approach, the classes of finding and the remediation patterns under NDA.
Tell us what your AI system does and what it is connected to, and we will scope an engagement against the threats that actually apply to it.
Scope an engagement