ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI
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arXiv
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| Natura: | Preprint |
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2026
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| author | Tong, Haibo Zhao, Feifei Feng, Linghao Wu, Ruoyu Chen, Ruolin Jia, Lu Zhao, Zhou Li, Jindong Li, Tenglong Lin, Erliang Yang, Shuai Lu, Enmeng Sun, Yinqian Zhang, Qian Ruan, Zizhe Fan, Jinyu Yue, Zeyang Wu, Ping Li, Huangrui Sun, Chengyi Zeng, Yi |
| author_facet | Tong, Haibo Zhao, Feifei Feng, Linghao Wu, Ruoyu Chen, Ruolin Jia, Lu Zhao, Zhou Li, Jindong Li, Tenglong Lin, Erliang Yang, Shuai Lu, Enmeng Sun, Yinqian Zhang, Qian Ruan, Zizhe Fan, Jinyu Yue, Zeyang Wu, Ping Li, Huangrui Sun, Chengyi Zeng, Yi |
| contents | 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 dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in-depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing-AISI/ForesightSafety-Bench. The project website is available at https://foresightsafety-bench.beijing-aisi.ac.cn/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI Tong, Haibo Zhao, Feifei Feng, Linghao Wu, Ruoyu Chen, Ruolin Jia, Lu Zhao, Zhou Li, Jindong Li, Tenglong Lin, Erliang Yang, Shuai Lu, Enmeng Sun, Yinqian Zhang, Qian Ruan, Zizhe Fan, Jinyu Yue, Zeyang Wu, Ping Li, Huangrui Sun, Chengyi Zeng, Yi Artificial Intelligence Cryptography and Security Computers and Society 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 dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in-depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing-AISI/ForesightSafety-Bench. The project website is available at https://foresightsafety-bench.beijing-aisi.ac.cn/. |
| title | ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI |
| topic | Artificial Intelligence Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2602.14135 |