Estimating Worst-Case Frontier Risks of Open-Weight LLMs

Estimating worst case frontier risks of open weight LLMs

OpenAI says malicious fine-tuning tests on gpt-oss informed its decision to release the model. It trained gpt-oss for maximum biorisk with RL plus web browsing, and for cyber risk in an agentic coding CTF setup; the resulting models still underperformed OpenAI o3. The key signal is the evaluation method, because the post does not disclose exact scores, training scale, or release thresholds.

Why it matters: HKR-H/K/R all pass: the malicious-fine-tuning setup is novel, the paper gives two concrete eval environments, and the open-weight release debate is a live nerve. It stays at 80 because the post omits scores, training scale, and release thresholds.