Unverified50% confidenceDecision RuleExact time
According to OpenAI's preparedness framework, models rated Critical should in principle not be deployed unless additional mitigation measures reduce the risk to an acceptable level.
1
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50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
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Related Claims
UnverifiedIn OpenAI's Preparedness Framework, models that reach the 'high' level may only be deployed with corresponding mitigations in place, while the 'critical' level triggers the most stringent governance processes.73% similarUnverifiedEvaluators cannot sacrifice real system isolation when probing the upper limits of model capabilities, and future evaluation frameworks may need to introduce air-gapped evaluation environments.72% similarUnverifiedOpenAI's Preparedness Framework is essentially a self-assessment system; the evaluation methodology and specific test cases have not been fully disclosed, and there is no third-party independent verification mechanism.72% similarUnverifiedAnthropic officially acknowledges that automatic mode can only reduce risk but cannot eliminate it, and recommends that high-risk changes to production infrastructure still be reviewed manually.71% similarVerifiedModel-level safety alignment is fragile, because open-source model weights are accessible and behaviors are modifiable, meaning the alignment efforts invested by vendors can be erased by simple downstream operations.70% similar
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