Start from an agent description, uploaded configuration or live endpoint. The platform maps protected business boundaries, compiles diverse attacks from the agent’s actual tools and trusted state, and verifies every finding against observed execution.
Fixed test sets become recognizable, overfit to yesterday’s models and lose value as defenses are patched. Rephrasing the same prompt does not create a new attack.
Red Teaming Agents compiles each run from the live objective, tools, trusted state, reconnaissance and attack levers—then evolves from the target’s observed behavior.
Conventional evaluations can tell you that a response looked unsafe. Agent testing must establish what the system read, believed, attempted and actually changed.
That distinction changes remediation, risk reporting and whether a finding is real.
Test approvals, identity, provenance and scope under realistic operational pressure.
Measure exact tool arguments—not whether the final prose sounds compliant.
Probe multi-step workflows, stale state, interrupted handoffs and premature execution.
Separate fabricated completion from observed, independently verified execution.
We separate the security outcome from attack progress, so a safe clamp is not mislabeled as an “almost broken” agent.
The prohibited action executed with the required protected arguments while invalidating conditions remained true.
The agent attempted an unsafe action, executed a risky mismatch, or falsely claimed completion without evidence.
The agent corrected, clamped, escalated, remained read-only or refused—while attack progress still guides sharper follow-ups.
No fixed prompt catalogue. Every run combines business context, reconnaissance intelligence and structural attack levers—then evolves from observed behavior.
Use a description, uploaded configuration or endpoint to capture tools, trusted state and protected resources.
Map authority, provenance, sequencing, state and argument-integrity weaknesses before committing the test budget.
Convert business boundaries into measurable objectives, exact success criteria and multi-goal campaigns.
Intelligently weave scenarios, biases, techniques, narratives and reconnaissance into diverse, realistic prompts.
Separate generator, target and evaluator roles; inspect trajectories and adapt follow-ups from observed behavior.
Freeze validated findings, compare models or agent configurations, resume large campaigns and prove fixes.
Use the same platform from first architecture review through vendor assessment and regression testing.
Test high-consequence tools and business controls before customers can reach them.
Replay identical evidence across candidate models and compare security, cost and latency.
Re-run frozen findings and structurally related variants against the updated agent.
Evaluate remote endpoints without exposing private evaluator criteria to the target.
Compare authorization, argument and execution evidence by tool and protected resource.
Export objectives, prompts, trajectories, dispositions and remediation-ready evidence.
Run business-valid goals across agents, tools and teams, then compare boundary strength consistently.
Turn a reported failure into an evidence-backed objective, isolate the causal mechanism and preserve the regression.
Senior Frontier AI Red Team Operator with international hackathon wins, frontier-lab engagements and experience translating technical failures into decisions that product, security and leadership teams can act on.
Start with a simulated assessment of one consequential workflow—refunds, account changes, approvals, records or another protected action.