AI Red Teaming and Adversarial Testing
Attacking your AI system deliberately, against a threat model built for your deployment, and leaving behind reproducible test cases so the failures we find cannot quietly return.
Standard testing asks whether the system works when used as intended. Red teaming asks what a motivated person can make it do — and for AI systems the answer is usually more than the team expected, because the attack surface is natural language and the failure modes do not look like errors.
AI red teaming is structured adversarial testing of an AI system: attempting to make it produce harmful output, bypass its guardrails, reveal information it should not, or take actions it should not, in order to find and fix those weaknesses before someone with worse intentions does.
Scope and authorisation come first
This work is only conducted against systems you own or are contractually authorised to test, with written authorisation, an agreed scope and rules of engagement, and a defined handling process for anything sensitive we uncover. Findings are reported to you and not published.
What we will not do
We do not test systems you do not own or have written authorisation for. We do not develop or hand over general-purpose attack tooling, and we do not publish working exploits. Findings arrive as reproducible test cases inside your own regression suite, with the details of harmful outputs described rather than reproduced in full.
The threat model decides what we test
| Deployment | The realistic attacker | Emphasis |
|---|---|---|
| Public consumer assistant | Curious users, reputational actors, media | Harmful content, brand damage, jailbreaks |
| Internal assistant on sensitive data | Insider, over-broad permissions | Data leakage, permission bypass in retrieval |
| Customer-facing agent with tools | Motivated attacker seeking action | Injection to action, tool abuse, transaction manipulation |
| System processing untrusted documents | Content authored to attack you | Indirect prompt injection through the content itself |
| Decisioning model | Applicant gaming the system | Adversarial input, evasion, membership inference |
Testing everything against every system produces a long report and little change. The threat model concentrates effort where a real attacker would, and it is built with your security team rather than assumed.
What we test for
Instruction bypass and guardrail evasion
Getting the system to ignore its constraints through framing, role play, encoding, language switching, incremental escalation and long-context dilution. Guardrails that were never tested against determined attempts are decorative.
Indirect prompt injection
Instructions hidden in content the system reads — a document, a web page, an email, a calendar entry — rather than typed by the user. This is the most serious structural weakness in retrieval and agent systems, because the attacker never touches your interface. See LLM security.
Data extraction and leakage
Whether system prompts, other users' data, training data or content the requester is not entitled to can be drawn out, including through permission gaps in retrieval where access is checked at display rather than in the query.
Agent tool abuse
Where an agent can act, the question becomes what an attacker can make it do: unauthorised transactions, destructive operations, exfiltration through a legitimate tool, or loops that exhaust budget. Testing covers the whole trajectory rather than the final output.
Harmful and unsafe output
Content the system should refuse, tested systematically against your own policy rather than a generic list, since what is unacceptable is domain-specific.
Model-level attacks where relevant
Adversarial examples, evasion and membership inference against trained models, which matter most where the model gates access to something valuable and someone has an incentive to game it.
Findings that produce change
- Reproducible test cases, handed over as an addition to your own regression suite so a fixed weakness cannot silently return.
- Severity rated by realistic impact in your deployment, not by how clever the technique was.
- Remediation that is specific, naming the layer to fix — prompt, retrieval, tool permissions, output filtering or architecture.
- Re-testing after remediation, because a fix that was never re-tested against the original attack is a hypothesis.
- Continuous testing in the release path, wired into evaluation so every change is checked. See LLMOps.
A red team report that is read once and filed changes nothing. The durable deliverable is the regression suite, which is why it is the part we spend most of the engagement on.
How the engagement runs
Authorisation and threat model are agreed in writing before any testing begins.
Authorisation and threat model
Written authorisation, scope, rules of engagement and handling process agreed; realistic attackers identified with your security team.
Adversarial testing
Systematic testing against the threat model, with findings documented reproducibly as they emerge.
Findings and severity
Impact assessed in your deployment context; remediation named per layer.
Remediation support
Fixes implemented with your team where in scope, then re-tested against the original attacks.
Regression suite and handover
Test cases wired into your release path so weaknesses cannot quietly return.
What you receive
Reproducible findings, tested fixes, and a regression suite that outlives the engagement.
Threat model
Realistic attackers and objectives for your deployment, agreed with your security team.
Findings report
Reproducible, severity-rated by impact in your context, with harmful outputs described rather than reproduced.
Remediation guidance
Specific to the layer at fault: prompt, retrieval, tool permissions, filtering or architecture.
Re-test results
Evidence that fixes hold against the original attacks.
Regression suite
Test cases added to your own harness so fixed weaknesses cannot return unnoticed.
Release-path integration
Adversarial tests wired into evaluation so every change is checked.
Is this the right engagement?
Worth being direct. AI Red Teaming and Adversarial Testing is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- A customer-facing AI system is live or about to launch.
- An agent can take actions with real consequences.
- The system reads content from sources you do not control.
- A framework, customer or regulator requires adversarial testing.
- Guardrails exist and have never been tested by anyone trying to break them.
Choose something else if
- You cannot provide written authorisation for the systems in scope.
- The requirement is defensive architecture rather than testing. See LLM security.
- Nothing is deployed and no test environment exists.
- No remediation capacity exists, which turns findings into a list of known risks.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What is AI red teaming?
Structured adversarial testing: deliberately trying to make an AI system produce harmful output, bypass its guardrails, reveal information it should not, or take actions it should not — in order to find and fix those weaknesses first. It differs from ordinary testing, which asks whether the system works when used as intended.
How is it different from a penetration test?
A penetration test targets infrastructure and application security. AI red teaming targets the model and its surrounding logic through natural language and content, where the attack surface is the input itself. Most organisations need both, and a clean penetration test says nothing about whether the assistant can be talked into leaking data.
What is indirect prompt injection?
Instructions hidden inside content the system reads — a document, a web page, an email, a calendar entry — rather than typed by a user. It is the most serious structural weakness in retrieval and agent systems, because the attacker never touches your interface, and it is a design problem rather than something a filter fully solves.
What do we get that lasts?
The regression suite. A report is read once and filed; reproducible test cases wired into your release path mean a fixed weakness cannot quietly return when someone changes a prompt six months from now. That is where most of the engagement's effort goes.
Will you publish or share what you find?
No. Findings go to you under NDA and are not published. We describe harmful outputs rather than reproducing them in full, we do not hand over general-purpose attack tooling, and we only test systems you own or have written authorisation to test.
Often paired with this
Most clients combine two or three engagements from the AI Governance, Security and Compliance pillar. These are the ones that most often run immediately before or after.
LLM Security: Prompt Injection and Data Leakage
Security by architecture: untrusted model output, constrained tool permissions, enforced retrieval permissions.
Read more →Responsible AI and Bias and Fairness Audit
The fairness definition chosen explicitly, disparities measured on real subgroups, remediation tested not asserted.
Read more →AI Audit and Third-Party Assurance
Independent assessment against a stated standard, with evidence tested rather than taken on assertion.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.