With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring existing text data from various domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether the private information is included in the training data and what privacy threats can these LLMs and their downstream applications bring. In this paper, we study the privacy threats from OpenAI's ChatGPT and the New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause new privacy threats. To this end, we conduct extensive experiments to support our claims and discuss LLMs' privacy implications.

翻译：随着大型语言模型（LLMs）的快速发展，许多下游自然语言处理任务在给定合适的提示后能够得到良好解决。尽管模型开发者和研究人员在对话安全方面投入了大量精力，以避免LLMs生成有害内容，但引导人工智能生成内容（AIGC）服务于人类福祉仍具挑战性。由于强大的LLMs正在消耗来自不同领域的现有文本数据（例如，GPT-3在45TB文本上训练），我们自然要怀疑训练数据中是否包含隐私信息，以及这些LLMs及其下游应用会带来哪些隐私威胁。在本文中，我们研究了OpenAI的ChatGPT及由ChatGPT增强的New Bing带来的隐私威胁，并表明与应用集成的LLMs可能引发新的隐私威胁。为此，我们进行了大量实验来支持我们的观点，并讨论了LLMs的隐私影响。