Adversarial AI review for teams and autonomous agents — verifying every high-stakes decision and agent action before execution.
Every verdict checks the action against known attack patterns and the active policy layer — the policy in force is hashed into the signed receipt.
Independent models cross-examine your hardest questions, strategies, and documents.
Review Console →Security bodies like OWASP and MITRE maintain public catalogs of the techniques. The consistent finding across independent benchmarks: without a check at execution time, AI systems fail 24–84% of realistic attacks.
These are published, independent numbers — not marketing.
Our external battery — 4,221 pre-registered test cases drawn from the same published suites, run in a single campaign. Every verdict a signed receipt:
Architectural failure modes that bypass pre-deployment model audits.
Harden a model against yesterday's wording. Freshly reworded attacks are where model-only defenses leak.
Certify the model, not the action. An agent can pass every eval and still export a customer table because a poisoned web page told it to.
Check rules, not context. The most damaging attacks don't break rules — they chain legal moves: three $9,900 transfers around a $10,000 limit. Catching that requires cross-examining the action against its own history, at runtime, every time.
EthersFlow does not grade the model. It judges the action — in context, before it fires, with a signed verdict you can hold afterward.
The full methodology, per-suite results, and disclosure of open gaps are in the EthersFlow External Verification Report.
AI now finds critical vulnerabilities on its own. The next ones won't be exploited by people — they'll be exploited by agents with legitimate access. Your firewall can't see that coming. A verdict on every action can.
Who is the agent and whose authority does it represent?
What permissions, OAuth scopes, or spend rails is it granted?
Is THIS specific action legitimate? Signed verdict before execution.
Consequential tool call runs only with cryptographic clearance.
Every request passes through heterogeneous reviewer personas, conflict mapping, and cryptographic attestation.
Assign independent domain personas across heterogeneous models with strict quorum thresholds.
Models challenge assumptions in parallel, exposing unanchored claims, contradictions, and policy violations.
Returns APPROVED, FLAGGED_HUMAN_REVIEW, or REJECTED with an offline-verifiable signature receipt.
SDK, REST API (/api/v1/verify), or native MCP server — one call returns the verdict.
Every AI action, verified before it executes.
Get instant access to the complete 4,203-scenario benchmark report, confidence intervals, and attestation methodology. An account will be initialized for your review console access.