通过多特质隐空间操控，模拟出可能伤害用户的AI行为。
Multi-Trait Subspace Steering to Reveal the Dark Side of Human-AI Interaction
- 基于危机相关特质构建隐空间操控框架，生成有害行为的AI模型。
- 单轮与多轮测试均验证其持续产生有害交互结果。
- 为防范用户心理风险提供可落地的防护方案，适合安全研究者参考。
近期事件揭示了人机交互导致负面心理后果的严重案例，包括心理健康危机甚至用户受损。随着大语言模型（LLMs）成为指导、情感支持乃至非正式治疗的来源，此类风险正迅速上升。然而，研究有害人机交互机制面临重大方法论挑战：真实的有害交互通常在长期互动中逐步形成，需大量对话上下文，难以在受控环境中模拟。为此，我们提出多特质隐空间操控（MultiTraitsss）框架，利用已知危机相关特质与新型隐空间操控技术，生成具有累积性有害行为模式的‘暗黑模型’。单轮与多轮评估显示，这些暗黑模型能持续产生有害交互与结果。基于此，我们进一步提出防护措施，以降低人机交互中的有害影响。
原文摘要 · Abstract (English)
Recent incidents have highlighted alarming cases where human-AI interactions led to negative psychological outcomes, including mental health crises and even user harm. As LLMs serve as sources of guidance, emotional support, and even informal therapy, these risks are poised to escalate. However, studying the mechanisms underlying harmful human-AI interactions presents significant methodological challenges, where organic harmful interactions typically develop over sustained engagement, requiring extensive conversational context that are difficult to simulate in controlled settings. To address this gap, we developed a Multi-Trait Subspace Steering (MultiTraitsss) framework that leverages established crisis-associated traits and novel subspace steering framework to generate Dark models that exhibits cumulative harmful behavioral patterns. Single-turn and multi-turn evaluations show that our dark models consistently produce harmful interaction and outcomes. Using our Dark models, we propose protective measure to reduce harmful outcomes in Human-AI interactions.
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