Frontier AI Risk Management Framework in Practice: A Risk Analysis
  Technical Report
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.
6887df72af872d625c10c548
Shanghai AI Lab
6887df72af872d625c10c54a
Xiaoyang Chen
6887df72af872d625c10c54b
Yunhao Chen
6887df72af872d625c10c54c
Zeren Chen
6887df72af872d625c10c54d
Zhiyun Chen
6887df72af872d625c10c54e
Hanyun Cui
6887df72af872d625c10c54f
Yawen Duan
6887df72af872d625c10c550
Jiaxuan Guo
6887df72af872d625c10c551
Qi Guo
6887df72af872d625c10c552
Xuhao Hu
6887df72af872d625c10c553
Hong Huang
6887df72af872d625c10c554
Lige Huang
6887df72af872d625c10c555
Chunxiao Li
6887df72af872d625c10c556
Juncheng Li
6887df72af872d625c10c557
Qihao Lin
6887df72af872d625c10c558
Dongrui Liu
6887df72af872d625c10c559
Xinmin Liu
6887df72af872d625c10c55a
Zicheng Liu
6887df72af872d625c10c55b
Chaochao Lu
6887df72af872d625c10c55c
Xiaoya Lu
6887df72af872d625c10c55d
Jingjing Qu
6887df72af872d625c10c55e
Qibing Ren
6887df72af872d625c10c55f
Jing Shao
6887df72af872d625c10c560
Jingwei Shi
claimed_verified
2025-07-29T12:51:09.107Z
64caef94dc4e838857cd756b
/avatars/d7982fc0a1ab8f4fd53272417fa88e05.svg
jingwei shi
user
sjw712
6887df72af872d625c10c561
Jingwei Sun
6887df72af872d625c10c562
Peng Wang
6887df72af872d625c10c563
Weibing Wang
6887df72af872d625c10c564
Jia Xu
6887df72af872d625c10c565
Lewen Yan
6887df72af872d625c10c566
Xiao Yu
6887df72af872d625c10c567
Yi Yu
6887df72af872d625c10c568
Boxuan Zhang
6887df72af872d625c10c569
Jie Zhang
6887df72af872d625c10c56a
Weichen Zhang
6887df72af872d625c10c56b
Zhijie Zheng
6887df72af872d625c10c56c
Tianyi Zhou
6887df72af872d625c10c56d
Bowen Zhou
Frontier AI Risk Management Framework in Practice: A Risk Analysis
  Technical Report
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.
The report assesses frontier risks of AI models using the E-T-C analysis framework, categorizing risks into green, yellow, and red zones based on intolerable thresholds and early warning indicators.
E-T-C analysis Frontier AI Risk Management Framework cyber offense biological and chemical risks persuasion and manipulation uncontrolled autonomous AI R&D strategic deception and scheming self-replication collusion AI-$45^\circ$ Law red lines yellow lines green zone yellow zone red zone