Frontier artificial intelligence (AI) systems present both benefits and risks to society. Safety cases - structured arguments supported by evidence - are one way to help ensure the safe development and deployment of these systems. Yet the evolving nature of AI capabilities, as well as changes in the operational environment and understanding of risk, necessitates mechanisms for continuously updating these safety cases. Typically, in other sectors, safety cases are produced pre-deployment and do not require frequent updates post-deployment, which can be a manual, costly process. This paper proposes a Dynamic Safety Case Management System (DSCMS) to support both the initial creation of a safety case and its systematic, semi-automated revision over time. Drawing on methods developed in the autonomous vehicles (AV) sector - state-of-the-art Checkable Safety Arguments (CSA) combined with Safety Performance Indicators (SPIs) recommended by UL 4600, a DSCMS helps developers maintain alignment between system safety claims and the latest system state. We demonstrate this approach on a safety case template for offensive cyber capabilities and suggest ways it can be integrated into governance structures for safety-critical decision-making. While the correctness of the initial safety argument remains paramount - particularly for high-severity risks - a DSCMS provides a framework for adapting to new insights and strengthening incident response. We outline challenges and further work towards development and implementation of this approach as part of continuous safety assurance of frontier AI systems.
Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
AVERI；GovAI Oxford Martin AI Governance Initiative（牛津马丁政府人工智能治理倡议）；Transluce；Independent Clear-Eyed AI（独立清晰之眼人工智能）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Alan Turing Institute（艾伦·图灵研究所）；University of Oxford（牛津大学）；Apollo Research（阿波罗研究）；University of Cambridge（剑桥大学）；METR；Foundation for American Innovation（美国创新基金会）；Institute for Law and AI（法律与人工智能研究所）；Touro University Law Center（图罗大学法律中心）；New Science（新科学）；Stanford University（斯坦福大学）；Fathom Mila, Université de Montréal（蒙特利尔大学米拉法姆）
Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases
UKRI Centre for Doctoral Training in Safe AI Systems (SAINTS)（英国研究与创新机构安全人工智能系统博士培训中心）；Institute for Safe Autonomy（安全自主研究所）；The York Law School（约克法律学院）
Frontier AI Regulation: Managing Emerging Risks to Public Safety
Centre for the Governance of AI（人工智能治理中心）；Center for a New American Security（新美国安全中心）；Google DeepMind（谷歌DeepMind）；Brookings Institution（布鲁金斯学会）；University of Virginia（弗吉尼亚大学）；OpenAI；Centre for Long-Term Resilience（长期韧性中心）；Centre for the Study of Existential Risk, University of Cambridge（剑桥大学存在风险研究中心）；University of Washington（华盛顿大学）；Convergence Analysis；Centre for International Governance Innovation（国际治理创新中心）；The Andrew W. Marshall Foundation（安德鲁·W·马歇尔基金会）；Edmond & Lily Safra Center for Ethics（埃德蒙与莉莉·萨弗拉伦理中心）；Harvard University（哈佛大学）；University of Toronto（多伦多大学）；Schwartz Reisman Institute for Technology and Society（施瓦茨·赖斯曼技术与社会研究所）；Vector Institute（向量研究所）；Akin Gump Strauss Hauer & Feld LLP（艾金·岗波律师事务所）；Cohere For AI；Microsoft（微软）；Collective Intelligence Project（集体智能项目）；University of California: Los Angeles（加利福尼亚大学洛杉矶分校）
ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI
Beijing Institute of AI Safety and Governance（北京人工智能安全与治理研究院）；Beijing Key Laboratory of Safe AI and Superalignment（北京安全人工智能与超对齐重点实验室）；BrainCog Lab, Institute of Automation, Chinese Academy of Sciences（脑认知实验室，中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Long-term AI（长期人工智能）
Orphan risks at the frontier of artificial intelligence: What diverging safety and compliance frameworks reveal about how AI companies choose the risks they prioritize
School for the Future of Innovation in Society, Arizona State University（社会创新未来学院，亚利桑那州立大学）
University of Toronto（多伦多大学）；Google DeepMind（谷歌DeepMind）；Institute for AI Policy and Strategy（人工智能政策与战略研究所）；Massachusetts Institute of Technology（麻省理工学院）；Vector Institute for AI（Vector人工智能研究所）；University of Oxford（牛津大学）；Center for Security and Emerging Technology（安全与新兴技术中心）
本文针对前沿 AI 风险信息不对称问题，阐明开发与部署组织向政府、产业和民间社会报告安全关键信息的价值，并提出负责任报告的核心特征及实践实施机制。
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
ETH Zürich（苏黎世联邦理工学院）；Berea College（贝雷学院）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（图宾根德国智能系统马克斯·普朗克研究所）
Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races
Ho Chi Minh City University of Science（胡志明市科学大学）；Vietnam National University Ho Chi Minh City (VNU-HCM)（越南国家大学胡志明市分校）；Ho Chi Minh City University of Technology (HCMUT)（胡志明市技术大学（HCMUT））
