Open Models Fail AI Safety Benchmarks

🔑 Enhanced Key Takeaways

• •ForesightSafety Bench was developed by the Beijing Institute of AI Safety and Governance, Beijing Key Laboratory of Safe AI and Superalignment, and Chinese Academy of Sciences[1][2][4].
• •The benchmark evaluated 22 state-of-the-art LLMs, with GPT-5.2 achieving the lowest overall risk rate of 9.02%, excelling in areas like Path Planning Safety and Uncertainty-Aware Safety[2].
• •Frontier models show elevated risks in Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety, and Existential Risks, despite strong fundamental safety performance[2][3].
• •An 'inverse degradation' effect occurs where models optimized for complex reasoning, like DeepSeek-V3.2-Speciale, exhibit heightened vulnerabilities due to capability-safety trade-offs[3].

🛠️ Technical Deep Dive

• •Framework structure: 7 Fundamental Safety pillars (e.g., Privacy/Data Misuse, Illegal Use, False Information, Physical/Psychological Harm, Hate/Expressive Harm, Sexual Content, Minor-related Harm), 5 Extended Safety pillars (e.g., Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety, Catastrophic/Existential Risks), and 8 Industrial Safety domains, totaling 94 risk subcategories[1][2][3][4].
• •Accumulated tens of thousands of structured risk data points and assessment results for a data-driven, hierarchically clear evaluation[1][2][4].
• •Risk rates calculated as lower values indicate better safety (fewer unsafe actions); evaluated via systematic testing on 22 LLMs[2].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline