Anthropic concluded in the April Mythos Preview alignment risk update that the model “does not possess any unknown propensities that would increase alignment risk.” The report argues that if Mythos Preview were coherently misaligned[1], it likely would have been detected by the assessment (following Anthropic, I will call this “reliability of the assessment”[3]).

While I agree with the report on the above bottom-line conclusions (substantially on priors), I think there are gaps in its argument which weaken the current assessment and might invalidate future assessments. In particular, the report often uses weak evidence to justify reliability.

The report gives fairly weak experimental evidence for Mythos Preview having insufficient capabilities to evade monitoring. The model is plausibly often eval-aware and underelicited in the relevant capability evaluations. So, it might silently sandbag if coherently misaligned, or unintentionally underperform if otherwise misaligned.
1. This limitation is important: one could argue that lack of covert capabilities for sophisticated sabotage (a subset of the capabilities I discuss here) is the single most load bearing argument in alignment risk reports.
2. Authors of the report could have made calibrated guesses about Mythos Preview's covert capabilities, especially for covert sabotage, based on other [...]

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Outline:

(03:16) How reliability fits into the overall safety argument

(05:22) Reliability claims by AI companies

(05:59) Reliability claims by external evaluators

(06:37) Alignment assessments are less reliable than developers claim

(07:21) 1: Measuring capabilities to covertly undermine alignment assessments

(10:15) Issues with evaluation awareness

(13:39) Issues with underestimating covert capabilities

(16:45) Issues with sandbagging rule-out

(19:20) 2: Stress-testing alignment assessments with auditing games

(20:26) An auditing failure with Mythos