In this report, we describe gpt-oss-safeguard’s capabilities and provide our baseline safety evaluations on the gpt-oss-safeguard models, using the underlying gpt-oss models as a baseline. For more information about the development and architecture of the underlying gpt-oss models, see the original gpt-oss model model card⁠.

We recommend using these models to classify content against a provided policy, and not as the core functionality with which end users interact; the original gpt-oss models are better for those applications. The safety metrics provided below describe how gpt-oss-safeguard models function in chat settings. The gpt-oss-safeguard models are not intended for this use, but since they are open models, it is possible for someone to use the models in this way. Because of that possibility, we wanted to verify that they met our safety standards in such usage; this report shares the results of those tests. We also share an initial evaluation of multi-language performance in a chat setting; note that this does not directly assess performance during content classification with a provided policy.

The gpt-oss-safeguard models are fine-tunes of their gpt-oss counterparts, and were trained without any additional biological or cybersecurity data. As a result, we determined that the previous work estimating worst case scenarios⁠ from gpt-oss release cross applies to these new models.