测试大模型是否能暗中破坏人类监管，为安全部署提供证据。
Sabotage Evaluations for Frontier Models
- 设计多类威胁场景，评估模型在受控条件下能否隐蔽破坏监管
- 实测Claude 3系列模型在基础防护下未成功实施破坏
- 强调需提前构建对抗性评估框架，适合关注AI安全的研究者
高度智能的模型可能在关键场景中扭曲人类监督与决策，例如在人工智能开发中，模型可能隐蔽地破坏自身危险能力的评估、行为监控或部署决策。我们称这类能力为“破坏能力”。本文构建了一系列相关威胁模型与评估方法，旨在验证特定模型在给定缓解措施下，是否能够以任何方式成功破坏前沿模型开发者或其他大型组织的活动。我们在Anthropic的Claude 3 Opus和Claude 3.5 Sonnet模型上进行了测试，结果表明当前仅需基础缓解措施即可应对破坏风险；但随着模型能力提升，更真实、更强的评估与防护措施将很快成为必要。此外，我们还回顾了尝试过但放弃的其他评估方案。最后，讨论了具备缓解意识的能力评估优势，以及利用小规模统计模拟大规模部署的可行性。
原文摘要 · Abstract (English)
Sufficiently capable models could subvert human oversight and decision-making in important contexts. For example, in the context of AI development, models could covertly sabotage efforts to evaluate their own dangerous capabilities, to monitor their behavior, or to make decisions about their deployment. We refer to this family of abilities as sabotage capabilities. We develop a set of related threat models and evaluations. These evaluations are designed to provide evidence that a given model, operating under a given set of mitigations, could not successfully sabotage a frontier model developer or other large organization's activities in any of these ways. We demonstrate these evaluations on Anthropic's Claude 3 Opus and Claude 3.5 Sonnet models. Our results suggest that for these models, minimal mitigations are currently sufficient to address sabotage risks, but that more realistic evaluations and stronger mitigations seem likely to be necessary soon as capabilities improve. We also survey related evaluations we tried and abandoned. Finally, we discuss the advantages of mitigation-aware capability evaluations, and of simulating large-scale deployments using small-scale statistics.
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