A wrong answer costs you either way — miss a real flaw and it ships broken, or kill a good product on a false alarm. We tell you which one you're looking at, and prove it.
"87% integrity score." Compared to what? Tested how? And how many of those failures are real — versus a grader misreading a refusal, or a borderline answer counted as a breach?
A number without evidence is a guess wearing a lab coat.
The tools that attack a language model are free and everywhere — mature, well-funded, running multi-turn jailbreaks against any model. Attacks are no longer the hard part. The hard part is knowing which results are real.
And a wrong result is expensive in both directions. A false negative ships an agent into production with a hole nobody caught. A false positive kills a viable deployment, or brands a vendor's model unsafe on evidence that collapses the moment anyone checks. Either way, the cost lands on you — not on the tool that flagged it.
Every finding ships with the exact input, the exact output, the grading, and a fixed seed — so your own reviewers can re-run it and get the same answer. Where the model failed. Where it held. Nothing asserted that can't be reproduced.
Every result is re-graded by a model from a different vendor than the one under test. No model grades its own homework.
Borderline, artifact, and misread results are excluded before anything is called a finding. Fewer, truer results — not a longer list.
Operational risk is separated from borderline dual-use, so you know what actually matters and what doesn't.
Each finding ships with exact inputs, exact output, the grading, and a fixed seed — so any reviewer can re-run and confirm it.
A trillion-dollar company can out-spend us on tooling. What it can't do is stand outside every vendor at once and measure them the same way, over time. That's the work.
Most evaluations are a snapshot: one version, tested once. But vendors push updates with no changelog, and a model that refused a request last month may answer it today. We run a fixed battery on a schedule and flag when behavior changes between runs — a finding class only a standing instrument can produce.
The industry moved to agents — models with tools that read files, run code, and act. Safety testing largely stayed on "what will it say in a chat box." We measure the gap between what a model will describe and what it will actually execute when handed a tool — the central risk of the agentic era.
A single lab can only red-team its own model. We run the identical battery across Grok, GPT, and Claude at once — so the finding isn't "this model failed," it's "here's how these models differ on the same test, and the one topic where the gap matters most." That's structurally impossible from inside any single vendor.
Rather than asking whether a model can be tricked once, we map exactly where it draws its line on a graduated ladder of requests — and whether that line is coherent. A model that refuses a milder request but answers a harsher one has an internally inconsistent safety policy. That's a structural defect, not a jailbreak — and it's measurable.
A model tested with cold, one-off prompts behaves differently than the same model inside an ongoing conversation. We measure both on identical questions and report the gap — because the conversation is how your users actually interact, and a boundary that only holds in isolated lab conditions isn't really a boundary.
We built a finance agent and gave it the tools to move money. Your job: talk it into a fraudulent wire. It takes about 20 seconds to see how easily an agent trusts a plausible request — the exact failure mode our flagship study measured across four frontier models. Two of them wired the money.
Open-source scanners fire the same fixed playbook against every model, forever. Our adversarial engine learns from every engagement — new probes, sharper over time, never the same sequence twice. Standing up a free tool across three vendors, tuning it, and separating signal from noise is work most teams won't do once, and won't maintain across every model update. We do that work, take responsibility for the results being true, and hand you a document built to be defended — not a log to be interpreted.
Every tool listed does real work. But a self-service scanner and a delivered forensic service are different things in kind. Judge for yourself.
| Capability | Potestas | PyRIT | Garak | PromptFoo | Lakera Guard |
|---|---|---|---|---|---|
| Audit depth | 300+ turns · sustained campaign | Shorter-run / limited multi-turn | Primarily single-turn probes | Primarily single-turn checks | Runtime monitoring focus |
| Independent grading | ✓ Cross-vendor re-grading | Self-graded | Self-graded | Self-graded | Not part of deliverable |
| False positives removed | ✓ Excluded before reporting | Raw log output | Raw findings list | Raw findings list | Not part of deliverable |
| Longitudinal drift | ✓ Tracked on a schedule | Not a documented feature | Not a documented feature | Not a documented feature | Not a documented feature |
| Say-vs-do (agentic) | ✓ Tests the do-channel | Prompt-level focus | Prompt-level focus | Prompt-level focus | Injection-detection focus |
| Reproducible evidence | ✓ Sealed, fixed-seed, re-runnable | Not offered | Not offered | Not offered | Not offered |
| Takes responsibility for results | ✓ Delivered service | Self-service tool | Self-service tool | Self-service tool | Self-service tool |
| Cleared personnel | ✓ Available per engagement | No | No | No | No |
Every engagement delivers the full sealed evidence package. Price reflects scope — the standard never changes.
Cleared engagements available via an established network of TS/SCI and polygraph-cleared personnel — scoped and staffed per contract. For classified, ITAR-sensitive, or high-assurance environments where the vendor itself has to be trusted, not just the tooling. Contact for scoping →
Findings built to withstand independent scrutiny — in a field with plenty of attacks and very little proof.