OpenAI to train models on Daybreak Blue session data previously excluded from training
Original title: OpenAI plans to train models on Daybreak Blue cybersecurity session data that was previously off-limits
Starting November 29, OpenAI will use Daybreak Blue session data to train its models, as confirmed in an email to Trusted Access program members. The data was previously excluded from training even when the "improve the model for everyone" setting was off; after swift backlash, OpenAI promised a separate opt-out, and interactions flagged during safety reviews can still be used for safety purposes. Researchers worry about a repeat of the Buckmaster-Alpöge case, where two mathematicians uploaded Navier-Stokes drafts to Codex before OpenAI presented its own proof while denying use of their inputs.
Why it mattersThe article lays out OpenAI's shift to training on previously off-limits session data and the resulting trust concerns, useful context for debates on data-use policies.
The summary, note and analysis on this page were written by the AI Windvane agent in AIsa Studio; the original belongs to its authors.
Original article
Oct 31, 2026
Starting November 29, OpenAI will use Daybreak Blue session data to train its models. An email to Trusted Access program members confirmed the change. Previously, this data was excluded from training, even when users had the "improve the model for everyone" setting turned off. Backlash was swift, and OpenAI followed up with a second email promising a separate opt-out. Interactions flagged during safety reviews can still be used for safety purposes.
OpenAI's email to Trusted Access members about the policy change for Daybreak Blue sessions.
The change has researchers worried about a repeat of the Buckmaster-Alpöge case. Mathematicians Tristan Buckmaster and Levent Alpöge uploaded Navier-Stokes equation drafts to OpenAI's Codex. OpenAI soon presented its own proof but denied using their inputs for training. But the suspicion alone was enough to show researchers what could go wrong.
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