Make Sure Your Model Can't Be Tricked Into Leaking

Verified, Not Trusted by Default. We test whether your model can be tricked into leaking training data, system prompts, or other users’ data — and whether its outputs can be manipulated into something harmful before they reach a customer.

Models can inadvertently expose training data, internal prompts, or one user’s data to another. These failures often aren’t caught until a customer or researcher finds them first.

Structured attempts to extract training data, system prompts, or cross-user information the model shouldn’t reveal.

Testing whether model outputs can be manipulated into harmful, biased, or brand-damaging content before it reaches an end user.

Before you build on a third-party foundation model or fine-tuned variant, we assess it for known vulnerabilities, licensing risk, and provenance — so you know what you’re actually inheriting.

Not Sure Where to Start?

Take our free Texas AI Trust Readiness Assessment — a 10-minute, no-obligation scored report covering shadow AI exposure, governance maturity, and compliance gaps.