Expert services · AI red teaming

Use AI you can stand behind.

AI is going into the business faster than anyone can prove it’s safe to trust. We threat-model the system before we attack it: what it can do, what it can reach, who can shape its inputs. Then we go after the paths that matter.

Talk to a senior about AI red teaming →See how an agent gets hijacked →

Why us

We build the offensive AI. So we know how it breaks yours.

Every engagement covers the attacks everyone should test for: prompt injection, jailbreaks, data leakage, tool and agent abuse, mapped to the OWASP Top 10 for LLMs. That’s the floor.

Then we go past the checklist. Because we build tailored offensive AI in-house, we come at your models the way the people who mean it will, including the attacks that aren’t on any list yet.

The AI you adopt is one we’ve already tried to break.

Standard coverage as the floor · tailored offensive models past it

From our research: When chatbots strike →

Why AI needs its own test

Every model you run makes a call on an input someone else can shape.

Feed it the wrong input and it hands you the wrong answer, just as confidently:

Fraud modelwaves the fraud through

Vision modelmisreads the image

LLM or RAGleaks the data

Agenttakes the action

Nothing crashes, so nothing flags. Your usual testing never sees it.

The bigger problem

AI walks through the controls you already paid for.

Once AI sits in the middle, the controls you bought wave it straight through:

It carries valid credentials and looks like normal work, so every control treats it as trusted.

Take the boundary most teams lean on hardest.

For example: zero trust

Zero trust held. Your AI walked them in.

Every agent clears zero trust’s credential check — then:

Fault 01

Identity explosion

82 machine identities per human, none approved.

Fault 02

Least privilege, gone

It reasons its way into more access, and gets it.

Fault 03

Prompt injection

A hidden command, in data it’s trusted to read.

Zero trust only ever asks “can it?” It never asks “should it?”

Read the full breakdown — “The day zero trust died” →

What we red team

Model to agent to pipeline.

Wherever your AI lives and whatever it does, a senior operator leads the testing against your live system, from the first scoping conversation through to the debrief:

Threat model

Threat modelling of the system and its agents, operational design domain analysis, risk-framework evaluation. Every engagement starts here.

Adversarial testing — app & agent

Direct & indirect prompt injection
Jailbreaks, data leakage & insecure output
Agents with tool access, MCP servers, retrieval pipelines and agent-to-agent calls: what each can be coerced into doing

ML-Ops & pipeline assessment

ML frameworks & architecture (e.g. PyTorch)
CI/CD processes & data-provenance analysis
The hardware stack (e.g. GPUs)

Model capabilities evaluation

Offensive & defensive cyber-capability benchmarking
First- and third-party validation
Which AI genuinely earns a place in your stack

See what we’d find in your AI, in 10 days.Threat model first, then the attack. Fixed fee, scoped on the call.See the 10-day offer →

What you walk away with

You leave able to trust it.

Every finding is a reproduction you can run yourself: the exact input, what it does, and the change that shuts it. Close them, and it’s ready to use.

A catalog of attacks

Every working attack we landed, across app, agent, and pipeline.

The fixes that close them

Guardrails, scoping, isolation, and pipeline changes.

A verdict on your controls

Which of the controls you already bought still hold.

Mapped to the OWASP Top 10 for LLM Applications ↗, MITRE ATLAS and the NIST AI RMF, so your auditors and your board already know the words. Scoped to whatever you’ve got: one feature, or a full agent platform and the pipeline under it.