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[2602.14135] ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI
Summary
The paper presents the ForesightSafety Bench, a comprehensive framework for evaluating AI safety risks, addressing limitations in current evaluation systems and proposing 94 refined risk dimensions across various AI domains.
Why It Matters
As AI systems become increasingly autonomous, the need for robust safety evaluation frameworks is critical. This research addresses existing gaps in AI safety assessments, providing a structured approach to identify and mitigate potential risks, which is essential for the responsible development of AI technologies.
Key Takeaways
- The ForesightSafety Bench framework identifies 94 risk dimensions for AI safety.
- Current AI safety evaluations are limited and often fail to detect frontier risks.
- The framework includes assessments of mainstream advanced large models, revealing widespread vulnerabilities.
- It emphasizes the importance of addressing social, environmental, and existential risks associated with AI.
- The benchmark is designed to evolve dynamically, adapting to new challenges in AI safety.
Computer Science > Artificial Intelligence arXiv:2602.14135 (cs) [Submitted on 15 Feb 2026] Title:ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI Authors:Haibo Tong, Feifei Zhao, Linghao Feng, Ruoyu Wu, Ruolin Chen, Lu Jia, Zhou Zhao, Jindong Li, Tenglong Li, Erliang Lin, Shuai Yang, Enmeng Lu, Yinqian Sun, Qian Zhang, Zizhe Ruan, Zeyang Yue, Ping Wu, Huangrui Li, Chengyi Sun, Yi Zeng View a PDF of the paper titled ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI, by Haibo Tong and 19 other authors View PDF HTML (experimental) Abstract:Rapidly evolving AI exhibits increasingly strong autonomy and goal-directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control, and potentially irreversible. However, current AI safety evaluation systems suffer from critical limitations such as restricted risk dimensions and failed frontier risk detection. The lagging safety benchmarks and alignment technologies can hardly address the complex challenges posed by cutting-edge AI models. To bridge this gap, we propose the "ForesightSafety Bench" AI Safety Evaluation Framework, beginning with 7 major Fundamental Safety pillars and progressively extends to advanced Embodied AI Safety, AI4Science Safety, Social and Environmental AI risks, Catastrophic and Existential Risks, as well as 8 critical industrial safety domains, forming a total of 94 refined risk dim...