OpenAI Blog · Aug 5, 2025
Estimating worst case frontier risks of open weight LLMs
Reviewed by Errol Vogt, Site support technician & online learning analyst · original summary · editorial policy
Estimating worst case frontier risks of open weight LLMs. In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity. This update is relevant for small-office operators tracking changes in their tools.
Operator takeaway: For operators: review whether 'Estimating worst case frontier risks of open weight LLMs' affects your current setup before relying on it in production.
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