Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report

Shanghai AI Lab; :; Xiaoyang Chen; Yunhao Chen; Zeren Chen; Zhiyun Chen; Hanyun Cui; Yawen Duan; Jiaxuan Guo; Qi Guo; Xuhao Hu; Hong Huang; Lige Huang; Chunxiao Li; Juncheng Li; Qihao Lin; Dongrui Liu; Xinmin Liu; Zicheng Liu; Chaochao Lu; Xiaoya Lu; Jingjing Qu; Qibing Ren; Jing Shao; Jingwei Shi; Jingwei Sun; Peng Wang; Weibing Wang; Jia Xu; Lewen Yan; Xiao Yu; Yi Yu; Boxuan Zhang; Jie Zhang; Weichen Zhang; Zhijie Zheng; Tianyi Zhou; Bowen Zhou

arXiv · 2507.16534

Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report

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Abstract

To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, this report presents a comprehensive assessment of their frontier risks. Drawing on the E-T-C analysis (deployment environment, threat source, enabling capability) from the Frontier AI Risk Management Framework (v1.0) (SafeWork-F1-Framework), we identify critical risks in seven areas: cyber offense, biological and chemical risks, persuasion and manipulation, uncontrolled autonomous AI R\&D, strategic deception and scheming, self-replication, and collusion. Guided by the "AI-$45^\circ$ Law," we evaluate these risks using "red lines" (intolerable thresholds) and "yellow lines" (early warning indicators) to define risk zones: green (manageable risk for routine deployment and continuous monitoring), yellow (requiring strengthened mitigations and controlled deployment), and red (necessitating suspension of development and/or deployment). Experimental results show that all recent frontier AI models reside in green and yellow zones, without crossing red lines. Specifically, no evaluated models cross the yellow line for cyber offense or uncontrolled AI R\&D risks. For self-replication, and strategic deception and scheming, most models remain in the green zone, except for certain reasoning models in the yellow zone. In persuasion and manipulation, most models are in the yellow zone due to their effective influence on humans. For biological and chemical risks, we are unable to rule out the possibility of most models residing in the yellow zone, although detailed threat modeling and in-depth assessment are required to make further claims. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.

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Shanghai AI Lab, :, Xiaoyang Chen, Yunhao Chen, Zeren Chen, Zhiyun Chen, Hanyun Cui, Yawen Duan, Jiaxuan Guo, Qi Guo, Xuhao Hu, Hong Huang, Lige Huang, Chunxiao Li, Juncheng Li, Qihao Lin, Dongrui Liu, Xinmin Liu, Zicheng Liu, Chaochao Lu, Xiaoya Lu, Jingjing Qu, Qibing Ren, Jing Shao, Jingwei Shi, Jingwei Sun, Peng Wang, Weibing Wang, Jia Xu, Lewen Yan, Xiao Yu, Yi Yu, Boxuan Zhang, Jie Zhang, Weichen Zhang, Zhijie Zheng, Tianyi Zhou, Bowen Zhou. 2025-07-26. Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report. https://arxiv.org/abs/2507.16534

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