Loading…...
We measure which defenses actually stop which attacks against your LLM applications — and how easily an attacker works around them.
Attack-simulation tools are mature for network and endpoint attacks but lag on LLM-integrated applications. Existing scanners report one aggregate “coverage” number that hides which defense did the work. We built an engine that traces every finding back to the specific control that should have stopped it — refusal filters, rate limits, tool-access auth, secret scrubbing — so teams can choose defenses based on evidence instead of stacking opaque vendor modules.
Every run drives a version-locked library of attack probes against your target and traces each finding back to the specific control that should have stopped it. The probe library is fixed, not improvised per run, so any result can be reproduced from the published artifacts. For AI findings we go one step further: generate a suggested fix and replay the attack to confirm it actually closes the bypass.
See how the engine worksIndependent research and engineering team focused on adversarial evaluation of LLM applications. Methodology comes before product copy — our probe corpus and grading rubric are versioned and auditable.
Point the engine at your AI endpoint and get a prioritized, replay-validated fix list plus a signed evidence pack — in minutes, no sales call.
Start a free run