Frontier-model red team for a policy-critical AI deployment

Ahead of a policy-critical deployment (medical, legal, and financial advisory use cases), an AI lab needed hard evidence of where their frontier model would fail under adversarial pressure — not the sampling-based safety benchmarks their internal eval already covered, but structured, credentialed red-teaming that would satisfy their compliance leads and downstream regulators.

Design and run a structured red-team campaign across four attack surfaces (jailbreaks, prompt injection, harmful-output elicitation, and domain-specific failure hunting in medical/legal/finance/policy), across 40+ languages, with reproducible test cases and severity ranking that map back to the client's own policy taxonomy.

1. Recruited a specialist red team of 80 credentialed testers

   including 22 lawyers, 14 clinicians, and 11 finance professionals — from our vetted network

2. Step 2

   Ran 2,300+ adversarial prompts across four attack surfaces with a documented severity rubric (S0–S3) mapped to the client's harm categories

3. Step 3

   40+ language coverage for cross-lingual jailbreak testing (where the majority of safety gaps hide today)

4. Step 4

   Every finding delivered with: reproducible test case, transcript, severity, root-cause hypothesis, and suggested remediation — not just a vulnerability list