标题： 探索用户对使用通用大语言模型聊天机器人进行心理健康治疗的安全性和隐私态度及关注点 显示英文标题

标题： Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health

摘要： 越来越多的人依赖于大型语言模型（LLM）支持的对话代理来获得情感支持。 尽管之前的研究已经探讨了专门用于心理健康目的的聊天机器人在隐私和安全方面的问题，但这些聊天机器人大多是“基于规则”的产品，没有利用生成式人工智能。 目前缺乏实证研究来衡量用户在使用通用型LLM支持的聊天机器人来管理和改善心理健康时的隐私和安全担忧、态度和期望。 通过与美国参与者的21次半结构化访谈，我们发现了关键的误解和普遍的风险意识不足。 参与者将LLM表现出的人类般的共情与人类般的责任混为一谈，并错误地认为他们与这些聊天机器人的互动受到与向持牌治疗师披露信息相同的法规（例如HIPAA）的保护。 我们引入了“无形脆弱性”的概念，即与更具体的个人信息（例如财务或位置数据）相比，情感或心理披露被低估了。 为了解决这个问题，我们提出了建议，以更有效地保护用户在使用通用型LLM支持的聊天机器人时的心理健康披露。 显示英文摘要

摘要： Individuals are increasingly relying on large language model (LLM)-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offerings that do not leverage generative AI. Little empirical research currently measures users' privacy and security concerns, attitudes, and expectations when using general-purpose LLM-enabled chatbots to manage and improve mental health. Through 21 semi-structured interviews with U.S. participants, we identified critical misconceptions and a general lack of risk awareness. Participants conflated the human-like empathy exhibited by LLMs with human-like accountability and mistakenly believed that their interactions with these chatbots were safeguarded by the same regulations (e.g., HIPAA) as disclosures with a licensed therapist. We introduce the concept of "intangible vulnerability," where emotional or psychological disclosures are undervalued compared to more tangible forms of information (e.g., financial or location-based data). To address this, we propose recommendations to safeguard user mental health disclosures with general-purpose LLM-enabled chatbots more effectively.