MoorAI inspects AI prompts, files, and outputs for risk without the content ever leaving the device. That's not a policy we promise — it's how the agent is built, and the agent is open source, so you can confirm it line by line.
Most “privacy-first” AI security tools ask you to trust a data-handling policy. MoorAI removes the need for trust by removing the data from the equation. Every detection runs locally, on the endpoint. Before anything is evaluated, content is reduced to one-way hashes (djb2 for fast fingerprint matching, SHA-256 where collision resistance matters). A hash is a fingerprint, not the thing itself — there is no key, no reverse function, no server-side decode, because the plaintext was never sent anywhere to decode.
The console receives verdicts and metadata — what class of risk fired, when, on which device, mapped to which framework. It never receives the text that produced the verdict. And because the on-device agent is licensed AGPL-3.0, you don't take our word for it: read the source, run the agent under a network monitor, and confirm no content egresses. The license makes independent verification a right, not a favor.
| Leaves the device — verdicts + metadata | Never leaves the device |
|---|---|
| Risk classification (e.g. OWASP LLM01 match) | Prompt text |
| One-way hashes used for matching | File contents |
| Detection timestamp | Model output / responses |
| Device & policy identifiers | Any reconstructable plaintext |
| Severity & rule id | Decryption keys (there are none to send) |
The mechanism is the guarantee: you cannot leak what you never transmit. Verify it in the source, then confirm it on the wire.
There is exactly one way content is ever retained, and we state it plainly rather than bury it. An administrator can enable a capture tier — for incident investigation or compliance evidence. It is fenced accordingly:
If you never enable a capture tier, MoorAI never stores content. Full stop.
A privacy guarantee is worthless if the tool can't catch anything. MoorAI ships an on-device benchmark that exercises the detection engine against a labeled corpus — including an adversarial red-team set — and reports precision and recall per threat class, mapped to the OWASP LLM Top 10 and MITRE ATLAS. Run it yourself:
npm run benchmark
Results are written to docs/BENCHMARK.md. Because it runs locally against the shipped detection logic, the numbers you produce are the numbers we publish — no hidden test set, no vendor-tuned demo. Current adversarial red-team coverage: 97 / 97 cases caught.
We won't claim assurances we don't have. Two independent validations are planned; here's exactly what each will prove, and its honest status.
Honest status matters more than a badge. The content-free mechanism and the reproducible benchmark are available today; the audit and SOC 2 are commitments in flight.
Read the code, watch the wire, run the numbers — then decide.