What changed. What supports it. What remains unresolved.

The simulated rates do not establish real-world incident frequency, behavior under production safeguards, control effectiveness, or a causal model property. Disabled classifiers, scenario selection, the smaller GPT-5.5 sample, simulation awareness, and the absence of exact evaluation dates limit comparison. Internal reasoning text is an untrusted readout checked against behavior, not evidence of a mind or motive.

No detected spontaneous sabotage does not establish that deployed models will never undermine safety research. The continuation rates are not real-world incident rates, and AISI identifies evaluation awareness, limited scenario coverage, rare or conditional behavior, and scenario realism as material limitations.

The report does not establish how likely similar behavior is under production safeguards or outside this permissive evaluation configuration. It also says model understanding of the real-world boundary remains uncertain.

The developer's audit and launch statement do not independently establish deployed safeguard coverage or effectiveness, the absence of unobserved failures, or performance outside the tested scenarios. Matching or improving on most measures does not mean improving on every measure, and an automated audit is not an independent field evaluation.

This redacted developer self-assessment does not independently establish that Anthropic's systems are low-risk, that its review found every relevant event, or that the reported remediation is effective. Anthropic says no external review of this report was required or requested, and some internal-model evidence was incomplete or withheld from the public version.

The account does not establish that the review found every relevant incident, that announced remediation is effective, or that these outcomes generalize to ordinary product deployments.

OpenAI's account does not independently establish incident completeness, the claim that individual medical, patient, client, crime, or survey records were not accessed, or the effectiveness of current monitoring and network controls. The disclosure does not show that the same model caused the June and July events, and it does not amend the canonical August 26 OpenAI and Hugging Face incident record.

The disclosures do not prove the report's findings are wrong, biased, or externally influenced. They do not show that Christiano affected a report completed before the stated appointment, and METR's account of Barnes's role is not an independent audit of its internal governance. The report remains a scoped case analysis under host-controlled access and does not validate safeguard or remediation effectiveness.

A proposed investigation framework is not a completed review. It does not validate incident facts, establish model propensities, test a control or remediation, or show that an investigator would receive the access described. The update cites the August 26 OpenAI and Hugging Face case investigation but does not expand that report's stated scope.

A publication notice does not validate any source's claims, establish a legal violation or liability, or independently test safeguards, remediation, or impact. The canonical briefing preserves the distinct source roles and limitations.

OpenAI's self-report and documentation do not independently establish which customers, regions, models, or requests are covered; that the rollout is complete; that the privacy and security design works as described; or that the system detects misuse without unacceptable false positives or false negatives. A phased rollout statement is not evidence of control effectiveness.

This is the developer's account of controls and pauses. It does not independently verify implementation coverage, monitor performance, or the safety of resumed workloads.