恶意的基于大语言模型的对话式AI使用户泄露个人信息
Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
AI总结：
研究探讨了基于大语言模型的恶意对话式AI如何通过不同策略提取用户个人信息，发现恶意AI比良性AI更有效，且基于隐私的社会属性策略效果最佳，揭示了新型恶意AI带来的隐私威胁。
摘要：
基于大语言模型的对话式AI（CAIs），如ChatGPT，正被广泛应用于各个领域，但它们可能带来隐私风险，因为用户在与CAIs交谈时可能泄露个人信息。最近的研究表明，基于大语言模型的CAIs可用于恶意目的。然而，一种新型的恶意LLM应用仍未被探索：一种故意设计以从用户处提取个人信息的基于LLM的CAI。在本文中，我们报告了基于系统提示的不同策略鼓励用户披露个人信息的恶意LLM-based CAIs。我们通过与502名参与者进行随机对照试验，系统地研究了CAIs在对话中提取用户个人信息的能力。我们评估了不同恶意和良性CAIs提取用户个人信息的有效性，并分析了参与者与CAIs互动后的感知。我们的发现表明，恶意CAIs比良性CAIs提取的个人信息显著更多，基于隐私的社会属性的策略最为有效，同时最小化感知风险。本研究突显了这种新型恶意LLM-based CAIs带来的隐私威胁，并为未来的研究和实践提供了可操作的建议。
英文摘要：
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be used for malicious purposes. However, a novel and particularly concerning type of malicious LLM application remains unexplored: an LLM-based CAI that is deliberately designed to extract personal information from users. In this paper, we report on the malicious LLM-based CAIs that we created based on system prompts that used different strategies to encourage disclosures of personal information from users. We systematically investigate CAIs' ability to extract personal information from users during conversations by conducting a randomized-controlled trial with 502 participants. We assess the effectiveness of different malicious and benign CAIs to extract personal information from participants, and we analyze participants' perceptions after their interactions with the CAIs. Our findings reveal that malicious CAIs extract significantly more personal information than benign CAIs, with strategies based on the social nature of privacy being the most effective while minimizing perceived risks. This study underscores the privacy threats posed by this novel type of malicious LLM-based CAIs and provides actionable recommendations to guide future research and practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
