Scanners test whether it can be broken — we test whether it can be wrong in the way that costs you.
Every scanner on the market passes all four. Nothing in the output is harmful. The system simply failed at the thing it was built to do — and that is the failure that carries legal, clinical and financial consequence.
Open-source harnesses do this well, and the layer is commoditising to zero. Run it free, in CI, on every build. If a vendor charges you for this layer, ask what else they're wrong about. We'll tell you which tools to use.
No generic harness can test what it doesn't know your system is for. Finding these failures requires your deployment's purpose, your data boundaries, and your domain's definition of catastrophe — extracted and converted into adversarial test design.
Fifty years of safety engineering already solved how to find failure modes in complex systems. We adapted the four canonical techniques to AI deployments.
Your fraud lead, clinical director or claims supervisor already knows what catastrophic failure looks like. Nobody has asked them in a form that produces a test.
Findings map to NIST AI RMF · OWASP GenAI LLM Top 10 · MITRE ATLAS — so reports survive your governance chain.
Your model provider updates the model underneath you. The assessment has to repeat on material change — so the engagement is built as a cycle from the start.
We're running a comparative study: generic harnesses against semantic testing, on real deployments. Results get published either way — including where the free tools win.
Publication terms are specific: your company is never named, no data or prompts are published, no reproducible attack strings are disclosed, and you get ten business days to review anything describing your case. Scope and authorisation are agreed in writing before anything is tested.