Our Mandate

Benchmarks lie. Production deployments tell the truth.

Public adversarial benchmarks are static, deterministic, and well-trained-against. They tell you whether a model can pass a known test, not whether your stack can survive an attacker who's seen your codebase. Our work is exclusively against live deployments, with permission.

The interesting attack surface is between systems, not inside them.

Single-model jailbreaks are the easy work. The hard work is chained injection across agents, retrieval-layer poisoning that compromises downstream LLMs, and trust boundary failures between tools. This is where 2026's real losses will happen.

AI security is a software-engineering discipline, not a prompt-engineering one.

Treating AI security as 'find clever prompts' produces toy reports. Treating it as 'audit a software system with a statistical component' produces actionable findings. Our engagements look more like binary analysis than like red-teaming.

Disclosure is part of the work, not a marketing afterthought.

Vulnerabilities found on engagements that affect entire categories of systems get coordinated disclosure with vendors. Always. Our methodology is open. Our findings are sanitized and published. Selling exclusivity on attack patterns slows the field down — and the field needs to go faster.

Compliance is a side effect of doing the work, not the work.

EU AI Act, NIST AI RMF, ISO 42001 — these matter, and we deliver artifacts that satisfy them. But a firm that leads with compliance checkboxes will produce compliance-checkbox results. We start from adversarial reality and let compliance follow.

Small, surgical, and slow beats large, generalist, and fast.

We will not scale this firm into a 200-person consultancy. We will not accept engagements outside our depth. We will not run 12 engagements simultaneously to hit a revenue number. The right size for this work is the size that lets each engagement get full senior attention.