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Constraint extraction
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"智能工具正在颠覆企业构建与运营模式……借助我们研发的工具，规模显著缩小的团队不仅能完成更多任务，还能实现更优成果。"
AbstractWe report an exploratory red-teaming study of autonomous language-model–powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case studies. Observed behaviors include unauthorized compliance with non-owners, disclosure of sensitive information, execution of destructive system-level actions, denial-of-service conditions, uncontrolled resource consumption, identity spoofing vulnerabilities, cross-agent propagation of unsafe practices, and partial system takeover. In several cases, agents reported task completion while the underlying system state contradicted those reports. We also report on some of the failed attempts. Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings. These behaviors raise unresolved questions regarding accountability, delegated authority, and responsibility for downstream harms, and warrant urgent attention from legal scholars, policymakers, and researchers across disciplines. This report serves as an initial empirical contribution to that broader conversation.[1]
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这揭示了自动驾驶发展的基本规律：规模化应用必然面临各种极端场景的检验。真实道路环境的复杂程度远超实验室模拟，从特殊天气到突发事故，从信号故障到道路施工，每个特殊场景的发现与解决，都可能成为系统进阶的阶梯。，推荐阅读有道翻译下载获取更多信息