Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of categories with real-world examples. Using them, we conducted a scenario-based study where participants (N=34) compared manipulative and neutral LLM responses. Our results reveal that recognition of LLM dark patterns often hinged on conversational cues such as exaggerated agreement, biased framing, or privacy intrusions, but these behaviors were also sometimes normalized as ordinary assistance. Users' perceptions of these dark patterns shaped how they respond to them. Responsibilities for these behaviors were also attributed in different ways, with participants assigning it to companies and developers, the model itself, or to users. We conclude with implications for design, advocacy, and governance to safeguard user autonomy.

翻译：大型语言模型能够通过对话影响用户，从而催生出不同于传统用户体验暗黑模式的新形式。我们将LLM暗黑模式定义为对话中实施的操纵性或欺骗性行为。借鉴先前研究和人工智能事件报告，我们通过真实案例勾勒出多样化的类别体系。基于此，我们开展了一项情境实验，参与者（N=34）对操纵性与中立性LLM回应进行比较。研究结果表明：对LLM暗黑模式的识别常依赖于对话线索，如夸张的附和、带有偏见的表述或隐私侵犯行为，但这些行为有时也被常态化为普通辅助功能。用户对这些暗黑模式的认知直接影响其应对方式。关于行为责任的归因呈现多元化倾向，参与者将其分别归属于企业与开发者、模型本体或用户群体。最后，我们从设计倡导与治理监管层面提出维护用户自主权的应对策略。