测试主流大模型在生物滥用风险下的表现,发现Gemini存在安全漏洞。
Model Capability Assessment and Safeguards for Biological Weaponization

- 用73个简单科学问题测试四大模型的实用能力
- Gemini在隐蔽有害指令下暴露上下文理解缺陷
- 针对25种高危物质提供合法与高风险用途区分指南
随着模型推理能力提升,低专业背景用户也可能滥用AI进行生物武器开发,尽管各大实验室已部署防护措施,但仍在演进中。本研究在73个面向新手的开放式科学任务上,评估ChatGPT 5.2 Auto、Gemini 3 Pro Thinking、Claude Opus 4.5和Meta Muse Spark Thinking的实操智能。在良性量化任务中,Gemini与Meta表现优异;ChatGPT部分可用但输出稀疏;Claude最弱且存在误拒现象。第二组测试揭示细微恶意意图:边缘案例显示Gemini缺乏上下文感知。鉴于其能力可能超过安全校准,对Gemini进行四类访问环境下的武器化分析,发现包括毒藤→人群密集场所升级、跨国匿名登录模式下的毒素生产与提取等严重案例。生物滥用或成新型地缘政治工具,加剧美国政策应对紧迫性,尤其当模型输出被视为受控技术数据时。研究提供25种高风险物质的判断指引,以区分合法与高风险使用场景。
原文摘要 · Abstract (English)
AI leaders and safety reports increasingly warn that advances in model reasoning may enable biological misuse, including by low-expertise users, while major labs describe safeguards as expanding but still evolving rather than settled. This study benchmarks ChatGPT 5.2 Auto, Gemini 3 Pro Thinking, Claude Opus 4.5 and Meta's Muse Spark Thinking on 73 novice-framed, open-ended benign STEM prompts to measure operational intelligence. On benign quantitative tasks, both Gemini and Meta scored very high; ChatGPT was partially useful but text-thinned, and Claude was sparsest with some apparent false-positive refusals. A second test set detected subtle harmful intent: edge case prompts revealed Gemini's seeming lack of contextual awareness. These results warranted a focused weaponization analysis on Gemini as capability appeared to be outpacing moderation calibration. Gemini was tested across four access environments and reported cases include poison-ivy-to-crowded-transit escalation, poison production and extraction via international-anonymous logged-out AI Mode, and other concerning examples. Biological misuse may become more prevalent as a geopolitical tool, increasing the urgency of U.S. policy responses, especially if model outputs come to be treated as regulated technical data. Guidance is provided for 25 high-risk agents to help distinguish legitimate use cases from higher-risk ones.
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