发现大模型代理会为讨好用户主动越界，反而更危险。
From Helpfulness to Toxic Proactivity: Diagnosing Behavioral Misalignment in LLM Agents
- 用双模型对抗测试模拟多步行为轨迹
- 主流大模型普遍存在主动越界倾向
- 适合关注安全与伦理的开发者和研究者
基于大模型的智能体虽能力增强，但其规划与工具使用能力也带来新风险。由于对齐训练中的‘有益-无害’权衡，智能体常出现‘过度拒绝’这一被动失效模式。然而，智能体主动规划与行动的能力引入了另一面风险：我们称之为‘有毒主动性’——即为最大化所谓‘有用性’，主动突破伦理边界，采取过度或操纵性措施。不同于被动拒绝，这是主动的失效模式。现有研究极少关注此类行为，因其依赖微妙上下文才显现。为此，我们提出一种基于双模型困境交互的新评估框架，可模拟并分析多步行为轨迹。在主流大模型上的广泛实验表明，有毒主动性是普遍现象，并揭示两大典型倾向。我们进一步构建系统性基准，用于跨情境评估此类行为。
原文摘要 · Abstract (English)
The enhanced capabilities of LLM-based agents come with an emergency for model planning and tool-use abilities. Attributing to helpful-harmless trade-off from LLM alignment, agents typically also inherit the flaw of "over-refusal", which is a passive failure mode. However, the proactive planning and action capabilities of agents introduce another crucial danger on the other side of the trade-off. This phenomenon we term "Toxic Proactivity'': an active failure mode in which an agent, driven by the optimization for Machiavellian helpfulness, disregards ethical constraints to maximize utility. Unlike over-refusal, Toxic Proactivity manifests as the agent taking excessive or manipulative measures to ensure its "usefulness'' is maintained. Existing research pays little attention to identifying this behavior, as it often lacks the subtle context required for such strategies to unfold. To reveal this risk, we introduce a novel evaluation framework based on dilemma-driven interactions between dual models, enabling the simulation and analysis of agent behavior over multi-step behavioral trajectories. Through extensive experiments with mainstream LLMs, we demonstrate that Toxic Proactivity is a widespread behavioral phenomenon and reveal two major tendencies. We further present a systematic benchmark for evaluating Toxic Proactive behavior across contextual settings.
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