AI Red Teaming
Prompt injection, jailbreak, and data-leakage testing against your client-facing model or agent.
An LLM in production is a new attack surface with its own failure modes — ones traditional testing doesn't cover.
We adversarially test your model or agent for the ways it can be manipulated, jailbroken, or made to leak data. Where the model can take actions, we test what it can be tricked into doing.
What's included
Prompt injection — direct and indirect injection against your model and its tool integrations.
Jailbreak testing — attempts to bypass guardrails and elicit disallowed behavior.
Data leakage — probing for exposure of system prompts, training data, or other users' data.
Agent abuse — where the model can act, testing for unsafe or unauthorized actions.
Tested against recognised frameworks
Adversarial testing of AI systems is a young discipline, and a lot of what gets sold as "AI red teaming" is unstructured prompt-poking with no defined coverage. We work to published standards so that what we tested, and what we didn't, is stated plainly.
OWASP Top 10 for LLM Applications (2026) — the current edition from the OWASP GenAI Security Project, covering prompt injection, sensitive information disclosure, excessive agency, hidden context exposure and the rest of the list.
MITRE ATLAS — the adversarial threat landscape for AI systems, used to structure attack techniques and map findings to tactics your team may already track through MITRE ATT&CK.
OWASP Top 10 for Agentic Applications — applied where your system is an agent rather than a chatbot: one that calls tools, holds memory between sessions and takes actions with real consequences.
Standards define coverage; they don't do the testing. The frameworks tell us what classes of failure to go looking for, and every finding is still produced by an operator working against your actual system — with the specific prompt, the observed behaviour and the business impact written up so your team can reproduce it.
How the engagement runs
Every engagement follows the same four-stage path — scope, test, report, verify. Scope and price are fixed in writing before any testing begins, and a re-test of the findings is included.
Vectors we probe
Adversarial testing across the failure modes unique to LLM-powered systems — structured against the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Prompt injection
Direct and indirect injection through user input and untrusted content the model reads.
Jailbreaks
Bypassing guardrails to elicit disallowed or unsafe behavior from the model.
Sensitive-data leakage
Extracting system prompts, other users' data, or confidential context.
Insecure tool use
Abusing the model's connected tools, plugins, and function calls.
Excessive agency
Tricking an agent into unauthorized actions beyond its intended scope.
Training-data extraction
Probing for memorized data the model may reveal under pressure.
What you get
Executive summary
A plain-language overview of the model's risks and business impact for leadership.
Technical findings
Each successful attack ranked by risk, with the prompts and steps to reproduce it and guidance to fix it.
Remediation support
We stay available while you harden the model and its guardrails, not just hand over a PDF.
Free re-test
Once you've hardened the model, we re-test the specific findings to confirm they're closed.
Secure your AI before it ships.
Fixed scope, fixed price, and a report you can act on — with a free re-test once you've remediated.