Adversarial testing of the AI systems your org is shipping or running: prompt injection, agent tool-use abuse, ML pipeline tampering, training-data poisoning. Findings with verified reproduction and remediation guidance — for the threat surface a traditional pentest can't reach.
Traditional pentests don't test AI systems — they don't know how. The threat surface for an agent in production isn't a network port; it's a prompt, a tool inventory, a grounding source, a training run. A clean network pentest plus an unreviewed AI feature is how breaches happen now. AI red-teaming is its own discipline: testing the agent's authority boundaries, the model's jailbreak resistance, the pipeline's tampering surface. Different attack model, different methodology, different evidence.
The threat surface for an AI system is structurally different from a classical app. These six show up in every engagement that delivered real findings.
// AI red-team coverage — every scope
// what a useful engagement delivers. anything less is jailbreak theater.
Reports that produce remediation, not PDFs that get archived. We measure success by fix rate, not finding count.
We agree on scope, rules of engagement, and reporting format. Threat model the AI surface: agents, models, training pipelines, grounding sources, tool inventories. Output: a prioritized target list ranked by data sensitivity, authority, and exposure.
Systematic prompt injection, tool-abuse, exfiltration, and prompt-chaining tests against each prioritized system. Findings verified by reproduction; false positives filtered before they hit your inbox. Real-time escalation for critical findings — not at the end of the engagement.
Report includes verified reproduction, severity rationale, remediation guidance, and mappings to the EU AI Act risk categorization where relevant. Retest of remediated findings included at no extra cost. Optional: per-release engagements for AI systems on a high-change cadence.
Free initial scoping — 30 minutes to tell you what's in scope, what a realistic timeline looks like, and what a useful AI red-team report should contain.