Share What we observed What we observed Our assessment of attribution Why this matters How we responded What comes next What we observed Our assessment of attribution Why this matters How we responded What comes next We recently identified and disrupted a coordinated campaign designed to extract protected reasoning from our models, with the earliest observed activity occurring in the first week of July. This activity is consistent with adversarial distillation: the systematic and unauthorized use of one model's outputs or reasoning to help train, reproduce, or improve another model. Protected reasoning is the model's internal record for working through a task; extracting it can reveal information withheld from the final answer and help others reproduce the model's capabilities
Before publishing, we investigated the scope and potential impact, deployed our own mitigations, and shared with and took feedback from researchers and industry partners to ensure protections against this type of attack are in place. Additional mitigation and investigation work is continuing. We believe sharing what we have learned now will help the broader ecosystem strengthen its defenses
We saw operators attempt to extract protected reasoning in novel ways, including by copying encrypted reasoning from one conversation and asking a model in another conversation to decrypt and transcribe the hidden reasoning content
