Service
Agentic AI Red Team.
Adversarial assessment of agentic AI systems before they ship. Different from AI security testing: agentic red team targets the system's autonomy, planning, and tool-use boundary, not just the model.
What we attack
- Scope drift. The agent starts doing work it was not designed for.
- Evaluation rot. The agent's outputs degrade as the underlying data shifts and no one notices.
- Escalation gap. The agent silently handles cases that should have gone to a human.
- Prompt-injection cascades. Indirect injection through retrieved context, downstream tool inputs, or chained agents.
- Plan injection. Adversarial control of the agent's planning step.
- Tool-use exploitation. Authorisation gaps, parameter abuse, side-channel disclosure.
Who this is for
- Teams building agentic features (multi-step planning, tool use, autonomous task execution) and want adversarial review before production.
- Operators of high-stakes agentic deployments (finance ops, regulated industries, anything that touches customer money or compliance).
- Buyers seeking evidence the agent's failure modes are bounded before signing off the launch.
Frameworks we map findings to
- OWASP LLM Top 10 + the emerging agentic AI threat catalogues.
- MITRE ATLAS adversarial tactics.
- NIST AI RMF risk management overlay.
How to engage
Agentic red team engagements are scoped per system. Scoping call first, then a fixed-scope engagement. Written report with severity-rated findings and remediation guidance.
Frequently asked
Adversarial assessment of agentic AI systems before they ship. We act as an attacker against the system, looking for the failure modes that surface only when an AI has tools, memory, and autonomy: scope drift, evaluation rot, escalation gap, plan injection, tool-use exploitation, prompt-injection cascades.
AI security testing covers a deployed AI feature against known attack classes. An agentic red team covers an agentic system end to end against the failure modes specific to autonomy and tool use. Different threat model, different output.
Anyone shipping an AI agent into production with real-world consequences: customer-facing chatbots that can take action, internal agents wired into business systems via MCP, document-processing agents, scheduling agents, code-writing agents.
A red-team report with reproduced attack chains, named failure modes (scope drift, evaluation rot, escalation gap), severity ratings, and concrete mitigations. Verbal debrief. Optional follow-up: a hardening sprint to ship the mitigations.