How we break models before attackers do
Every engagement begins with zero assumptions about safety. We model the threat surface of the deployed architecture, execute attacks against the model itself, and reproduce every finding against production conditions.
Documented, reproducible, AI-specific
Threat Modeling
Attack Execution
Finding Reproduction
Every confirmed vulnerability is reproduced against production conditions and ranked by real exploitability—not theoretical severity lifted from a framework built for network infrastructure.
Adversarial inputs are constructed and run against the live model: prompt injection, data extraction, reasoning manipulation, and model inversion—executed, logged, and documented.
We map the model's architecture, data flows, and integration points to identify attack surfaces specific to the deployed system—not a generic checklist.
Traditional pen testing targets infrastructure perimeters. Our process targets the model's reasoning layer—how it interprets inputs, what it leaks, where its outputs can be coerced.
Built for the model's attack surface
Findings are scoped to your production system's specific deployment context. A vulnerability that can't be reached by a real adversary in your environment doesn't appear in the report.