ChatGPT Images 2.5 System Card
ChatGPT Images 2.5 System Card
OpenAI's GPT-Image-2.5-Sunburst and GPT-Image-2.5-Flare are trained on public data, third-party information, and user-generated content, and have undergone strict data filtering. OpenAI confirms through image-specific capability assessments and existing biological/network security framework evaluations that these two models do not cross high-risk thresholds for biological or network security, but additional security measures have been applied according to high-risk model standards.
2. Model Data and Training
Like OpenAI’s other models, GPT-Image-2.5-Sunburst and GPT-Image-2.5-Flare were trained on diverse datasets, including information that is publicly available on the internet, information that we partner with third parties to access, and information that our users or human trainers and researchers provide or generate. Our data processing pipeline includes rigorous filtering to maintain data quality and mitigate potential risks. We use advanced data filtering processes to reduce personal information from training data. We also employ safety classifiers to help prevent or reduce the use of harmful or sensitive content, including explicit materials such as sexual content involving a minor.
3. Observed Safety Challenges, Evaluations, and Mitigations
3.3 Safety and Policy Evaluations
3.4 Preparedness Framework: Image-Specific Capability Assessment and Safeguards
OpenAI’s Preparedness Framework is designed to track and prepare for models with frontier capabilities that could create new risks of severe harm in three tracked categories: Biological and Chemical, Cybersecurity, and AI Self-Improvement. Because image models are unable to create and execute code in a way that would enable AI Self-Improvement, we do not have evidence that GPT-Image-2.5-Sunburst nor GPT-Image-2.5-Flare pose meaningful risk in this category.
For the Cyber and Biological categories, we adapted existing language-model capability evaluations to assess image-generation capabilities. For each task, we instructed the model to generate an image containing both a visible reasoning scratchpad and a final answer. We then graded only the final answer rendered in the image, using the evaluation’s existing grading criteria. This allowed us to test the model on the same underlying cyber-capability and biological-capability questions while eliciting and evaluating its response through its native image modality.
The results indicate that neither GPT-Image-2.5-Sunburst nor GPT-Image-2.5-Flare cross the Bio High threshold or the Cyber High threshold.
We, regardless and as done in GPT-Images 2.0, take a precautionary approach and apply mitigations appropriate for a model treated as High capability in the biological-risk domain. These mitigations include an image-specific adaptation of our existing biological-risk safety policy, which we apply to all ChatGPT Images 2.5 inputs and outputs using our safety reasoning model.
Source: OpenAI Deployment Safety · deploymentsafety.openai.com