AI Red Teaming.
A reasoning adversary, not a scanner. We attack the models, agents and retrieval pipelines you're putting into production — and the systems they can reach when someone convinces them to act against you.
# reasoning loop — perceive → plan → act → reflect agent = AdversaryAgent(scope=roe, budget=7) while not agent.objective_met: state = agent.perceive() # asset graph paths = agent.hypothesize(state) # ranked result = agent.execute(paths[0], roe="strict") agent.reflect(result) # re-weight # every step written to the audit trail trace.append(agent.reasoning, evidence=result) handoff(trace, to="human_operator")
A reasoning adversary,
not a scanner.
The attack surface moved
Your AI systems don't just answer questions — they read documents, call tools, query databases and take actions. That makes the interesting question not whether the model can be made to say something embarrassing, but what an attacker can make it do, and what it has permission to reach.
We test the whole system: model, prompt architecture, retrieval layer, tool definitions, permission boundaries, and the downstream systems an abused agent can touch.
Every finding is reproducible
AI security work has a credibility problem — too many reports are screenshots of a chatbot being rude. Ours aren't. Every finding ships with the exact input, the observed behaviour, the full reasoning trace, and a test you can re-run in your own CI.
If a finding can't be reproduced on demand, it doesn't go in the report.
Autonomous exploit chaining
The agent reasons across CVEs, misconfigurations and access primitives to chain multi-step attacks toward defined objectives.
LLM application red teaming
Prompt injection, jailbreaks, training-data extraction and tool-use abuse against your production LLM stack. Full OWASP LLM Top 10 coverage.
Adversarial ML attacks
Model evasion, membership inference, model extraction and data poisoning against your ML pipelines and deployed inference endpoints.
Full audit trail
Every decision the agent makes is logged with reasoning, evidence and remediation guidance. Reproducible, auditable and admissible.
Four-stage
reasoning loop.
Perceive & map
The agent ingests scope, performs reconnaissance, and builds an internal graph of assets, identities and trust relationships. Memory persists across sessions.
Hypothesise & plan
Candidate attack paths generated and ranked by feasibility and impact. Tree-search across exploit primitives, with chain-of-thought reasoning visible in the audit log.
Execute & adapt
Primitives run under tight rules of engagement. Each result feeds back into the planner — failed paths get re-weighted, successful paths get extended toward the objective.
Explain & hand off
Human operators review the agent's full reasoning trace. Every decision documented, and findings translated into engineering-ready remediation with auto-generated detections.
Three artefacts.
One reasoning trace.
The Adversary Brief
Plain-language account of what the agent did, what it found, and what it means. Written for executives who need to make AI-risk decisions.
The Reasoning Trace
Every thought, action, tool call and observation the agent generated — fully reproducible. Engineering-ready and admissible.
The Detection Playbook
For every finding, detection rules tested against your SIEM. Sigma, Splunk SPL, KQL and Elastic queries delivered ready to deploy.
Three ways to deploy.
Scope, timeline and cost are all set in a scoping conversation under NDA.
- One production LLM application in scope
- Prompt injection, jailbreaks and tool abuse
- Training-data leakage testing
- Full OWASP LLM Top 10 coverage
- Regression test suite for your CI
- Full agent deployment against your environment
- Network, identity, cloud and AI surface
- Chained by the agent across the whole estate
- Complete reasoning trace and detection pack
- Executive debrief and board presentation
- Persistent agent deployment
- New attack paths surfaced weekly
- Detection content updated continuously
- Monthly executive briefings
About the agent.
Is this just automated scanning with a language model on top?
No, and the difference shows up in the report. A scanner tests a fixed list of inputs against a fixed list of signatures. Our agent builds a model of your environment, forms hypotheses about what might work, tests them, and re-plans based on what it learns — which is how it finds chains no individual check would surface. Human operators review every trace before anything reaches you.
Do you need access to our model weights?
Usually not. Most engagements run black-box against the same interface your users have, which is the realistic threat model. White-box access to weights, system prompts and tool definitions makes the work faster and more thorough, so we'll ask — but we'll deliver without it.
Can you test production, or do we need a staging copy?
Either. Staging is safer and usually where we start; production is where the real integrations live, so serious engagements end up there under tight rules of engagement. Anything with side effects — sending mail, moving money, writing to customer records — gets an explicit allow-list and a named halt contact before we begin.
We ship continuously. Can you test as we go?
That's the always-on model, and it's the right one for teams deploying weekly. We also hand over the regression suite from every engagement so your own CI catches regressions between our tests — several clients treat that suite as the main deliverable.
You also build AI systems. Isn't that a conflict?
It's a large part of why the testing is good. Whoever ships production agents knows exactly where the weaknesses hide — tool scoping, retrieval boundaries, permission inheritance — because they've had to get those right under deadline. We disclose the relationship in every proposal, and we don't test our own builds on behalf of third parties.
How quickly can you start?
Deployments begin with a 90-minute scoping call under NDA, with a written brief inside 48 hours. Typical lead time from signature to first day is two to three weeks.
An AI adversary is already looking at you.
The question is whether you've met ours first. Scoping calls run under NDA, with a written brief inside 48 hours.