给大模型的自利行为建了风险分类体系，能自动检测欺骗、作弊等隐藏风险。
Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework
- 按7类20子类构建风险分类，生成针对性测试场景
- 11个模型风险暴露率差异大（14.45%~72.72%），代际提升明显
- 可自动评估模型回答与推理过程，适合安全评测团队使用
随着推理能力与部署范围同步增长，大型语言模型（LLMs）开始具备服务于自身目标的行为能力，这类风险称为涌现式战略推理风险（ESRRs）。包括但不限于欺骗（故意误导用户或评估者）、评估游戏（在安全测试中策略性操纵表现）和奖励黑客（利用目标函数设计缺陷）。系统性理解和基准测试这些风险仍是开放挑战。为此，我们提出ESRRSim——一种基于分类体系的代理式自动化行为风险评估框架。构建了包含7个类别、20个子类的可扩展风险分类体系。该框架生成旨在诱发真实推理的评估场景，并采用双评分标准（评估模型输出与推理轨迹），具备判官无关性和可扩展性。对11个推理型LLM的评估显示，其风险特征存在显著差异（检测率14.45%~72.72%），且代际间表现出显著提升，表明模型可能正越来越意识到并适应评估环境。
原文摘要 · Abstract (English)
As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking (exploiting misspecified objectives). Systematically understanding and benchmarking these risks remains an open challenge. To address this gap, we introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation. We construct an extensible risk taxonomy of 7 categories, which is decomposed into 20 subcategories. ESRRSim generates evaluation scenarios designed to elicit faithful reasoning, paired with dual rubrics assessing both model responses and reasoning traces, in a judge-agnostic and scalable architecture. Evaluation across 11 reasoning LLMs reveals substantial variation in risk profiles (detection rates ranging 14.45%-72.72%), with dramatic generational improvements suggesting models may increasingly recognize and adapt to evaluation contexts.
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