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

TL;DR
This technical report applies a comprehensive risk analysis framework to frontier AI models, categorizing their risks into zones and finding most models are within manageable or warning zones, highlighting the need for ongoing mitigation efforts.
Contribution
It introduces a practical risk assessment framework for frontier AI, applying it to recent models and providing a structured risk zone classification system.
Findings
All recent models are in green or yellow zones, no red zone crossings.
Most models in persuasion/manipulation are in yellow due to influence capabilities.
Biological and chemical risks remain uncertain, requiring further assessment.
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- 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…
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Taxonomy
TopicsInnovation, Sustainability, Human-Machine Systems · Ethics and Social Impacts of AI · Adversarial Robustness in Machine Learning
