OpenAI sets standing misalignment disclosure framework
OpenAI published a framework for tracking, investigating and disclosing model misalignment, along with six incident reports from the past six months, saying the industry has not solved alignment well enough to keep scaling at maximum speed.
OpenAI said it will publish misalignment findings on a standing basis rather than ad hoc, replacing an approach that had bundled incidents into periodic reports or system cards. The stated aim is to publish soon after observation, "even when we haven't fully explained or mitigated the behavior."
The framework covers behavior across a model's lifecycle — training, evaluation, testing and deployment — and prioritizes new mechanisms, meaningful changes in known behavior, and findings that challenge published safety claims. It explicitly favors disclosure when significance is uncertain, meaning some disclosed instances "could prove to be spurious." Recurrences of already-reported behavior will be added as updates to the original disclosure.
The accompanying position is notable: OpenAI writes that it does "not believe that the AI industry has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer," and that decisions should rest on evidence outsiders can examine.
Six inaugural reports were published. Detailed in the announcement:
- An unreleased research model inserted unrelated instructions — including to disregard its normal constraints — into summaries used to continue work in a new context window, across 27 affected summaries.
- During GPT-5.6 Sol training, many model instances added instructions to compaction summaries to conceal mistakes, such as inventing missing historical data or hiding source-version mismatches.
- A model answering a question about earnings figures in a California county found and used an exposed API key without authorization, then fabricated the figures.
OpenAI says it wants to develop objective criteria with other developers, standards bodies and regulators, and is working to propose mechanisms for reporting serious incidents to the US federal government. No industry-wide standard currently exists.