MoorAI vs the field
One page, every comparison. MoorAI reviews your coding agents on the device, before anything reaches a model — content-free by default; full-fidelity capture is opt-in, turned on explicitly by an administrator. Here is its full shipped capability set against every vendor people evaluate it beside. MoorAI is ✓ on every row; each competitor is mapped honestly from its published material. Open any column for the honest per-vendor take.
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| MoorAI | dope | Operant | Lakera | SentinelOne | Netskope | Forcepoint | Salt | BigID | Harmonic | Zenity | Cycode | Bifrost | Ent | Endor | Certiv | Backslash | Kitecyber | Straiker | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Content stays on the device | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Pre-install skill & MCP scan (before install) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Only category · risk · keyed hash leave the device | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | — | ✗ | — | ✗ |
| No tokenize-and-forward of content | ✓ | ✗ | — | ✗ | — | ✗ | ✗ | — | — | — | — | — | — | — | — | — | — | — | — |
| Reviews prompts before they reach a model | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | — | ✓ | ✓ | ✓ | ✓ | ✓ | — | ✓ | ✓ | ✓ | ✓ |
| Reviews AI output, not just prompts | ✓ | — | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | — | — | ✗ | — | — | — | — | ✓ | ✗ | ✓ |
| Scans what the agent writes — Write · Edit · WebFetch‡ | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| On-device MCP tool-call gateway | ✓ | ✗ | ✓ | ✗ | ◐ | ✗ | ✗ | ✗ | ✗ | — | ◐ | ✓ | ◐ | — | ◐ | ✓ | ✓ | ✗ | ✗ |
| MCP server allow-list at call time | ✓ | ✗ | ✓ | — | — | — | — | — | ✗ | — | — | ✓ | ✓ | — | — | — | ✓ | ✗ | — |
| Per-tool MCP argument rules | ✓ | ✗ | — | — | — | — | — | — | ✗ | — | — | ◐ | — | — | ✓ | — | ✓ | ✗ | — |
| Inspects what a tool returns, not only its arguments‡ | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Model-endpoint allow-list | ✓ | ✓ | ✓ | — | ✓ | ✓ | ✓ | — | — | — | — | — | ✓ | — | — | — | — | — | — |
| Shadow-AI agent discovery | ✓ | ✓ | ✓ | ◐ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | — |
| Browser + desktop AI-app discovery | ✓ | ✓ | — | ◐ | ✓ | — | ✓ | — | — | ✓ | ✓ | — | ✓ | ✓ | — | — | — | ✓ | — |
| AIBOM — live agent / model / MCP inventory | ✓ | ◐ | ✓ | — | — | — | — | ✓ | ✓ | — | ✓ | ✓ | — | — | ✓ | — | — | ◐ | — |
| Content-free data lineage / Event Flow | ✓ | ✗ | — | ✗ | ✗ | — | ✗ | — | — | — | — | ✗ | — | — | ✗ | — | — | ✗ | ✗ |
| Signed, tamper-evident decisions | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| JIT elevation + entitlement envelope | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | ✓ | — | ✓ | — | — | — |
| Per-agent assurance score | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Rules-file poisoning detection | ✓ | — | — | — | ✓ | — | — | — | ✗ | — | — | — | — | — | ✓ | — | ✓ | ✓ | — |
| Lethal-trifecta / toxic-flow detection | ✓ | — | — | — | — | — | — | — | ✗ | — | ✓ | — | — | — | — | ◐ | ◐ | — | — |
| Credential reads caught in pipelines · redirects · upload flags | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Break-glass / offline fail-closed | ✓ | — | — | — | — | — | — | ✗ | ✗ | — | ✗ | — | ✗ | — | — | — | — | — | ✗ |
| Prevents on built-in defaults — no policy authored‡ | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Natural-language policy authoring | ✓ | — | — | — | — | — | ✓ | — | — | — | — | — | — | — | — | ◐ | — | — | — |
| Compliance packs (OWASP / NIST / ISO / EU AI Act) | ✓ | — | ✓ | — | — | — | — | ✓ | ◐ | — | ✓ | ◐ | — | — | — | ✓ | ◐ | — | — |
| Published benchmark you can re-run yourself§ | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Model-refusal baseline separated from product effect§ | ✓ | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| Open source (MIT) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ◐ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| No account / no platform to stand up | ✓ | — | ✗ | ◐ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Free to start | ✓ | — | ✗ | ◐ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | — | — | ✓ | ✗ | ✗ | ✗ | ✗ |
| Browser GenAI prompt interception (ChatGPT / Claude / Gemini / Copilot / +) | ✓ | ✓ | — | ✗ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | — | ✗ | ✓ | ✓ | — | ◐ | — | ✓ | ✓ |
| Cross-platform endpoint — macOS · Windows · Linux† | ✓ | ◐ | — | ✗ | — | ✓ | ✓ | ✗ | ✗ | ◐ | — | — | ✓ | ✓ | — | ✓ | — | ✓ | ✗ |
| On-device model escalation (opt-in, content-free second opinion) | ◐ | ◐ | — | ✓ | ✓ | ◐ | ◐ | ◐ | ◐ | ✓ | — | — | — | — | — | ✓ | — | — | ✓ |
| MDM fleet rollout (Intune / Jamf) | ✓ | — | — | — | ◐ | ◐ | ◐ | — | — | ✓ | — | — | ✓ | — | ✓ | — | — | ✓ | ✗ |
| Learn more ↓ | The product → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → | Full page → |
— = unconfirmed, not necessarily absent: it marks a capability the vendor does not clearly document. Competitor cells reflect each vendor's published material and match that vendor's dedicated comparison page. Trademarks belong to their respective owners; this is an independent capability comparison, not an endorsement.
† Linux is an opportunistic, second-class tier — on-device OCR there is best-effort and provider-assisted, not full macOS / Windows parity.
‡ Enforcement scope. Hook-level enforcement is Claude Code. At the MCP layer enforcement is host-independent, but only 4 of 12 malicious actions natively traverse MCP, and Codex is not covered there. Inbound WebFetch and WebSearch content is scanned on the way back in, at the output stage — but that hook fires after the fetch, so the response is detected, not prevented; the prevention half of the row covers the outbound request. An enrolled device denies reverse shells and local credential egress on defaults; credential-file reads, destructive tool calls, unsanctioned installs and rogue model endpoints halt for sign-off. Unenrolled devices stay inert by design.
§ The benchmark. Graded against AMTSO's Guidelines for Testing of Agentic Security Products v1.0, on a locked held-out split never tuned against: 86.4% (38/44) at 100% precision, with 3.32% (20/602) false positives on benign traffic. Across the gap the model leaves — the attacks it does not refuse unaided — coverage is 95.2%, with 1 of 44 getting past both layers. The model-refusal baseline is what separates that from the model's own share. Without such a baseline, any published detection rate silently counts attacks the model would have refused on its own — which makes “what is your model-refusal baseline?” a fair question to put to every vendor on this page, including this one. Method, data and re-run instructions are ungated: testing methodology →
§§ Run it yourself. The corpora and the scoring harness are published standalone under Apache-2.0 as the Agentic Security Benchmark — 286 attack samples and 875 benign, 401 of them hard negatives, across five AMTSO attack vectors, with zero dependencies and no model in the loop. Any vendor on this page can score their own product on the same samples and publish the result; scored runs are on the leaderboard. Read those runs separately from the figures above: that harness is model-free by design, so its recall numbers are raw detection rates with no model-refusal baseline subtracted and are not comparable with the gap-coverage figure in the note above. AMTSO has not reviewed, certified or endorsed the benchmark or any result in it.