Abstract
On December 4, 2025, Anthropic released Anthropic Interviewer, an AI tool for running qualitative interviews at scale, along with a public dataset of 1,250 interviews with professionals, including 125 scientists, about their use of AI for research. Focusing on the scientist subset, I show that widely available LLMs with web search and agentic capabilities can link six out of twenty-four interviews to specific scientific works, recovering associated authors and, in some cases, uniquely identifying the interviewees. My contribution is to show that modern LLM-based agents make such re-identification attacks easy and low-effort: off-the-shelf tools can, with a few natural-language prompts, search the web, cross-reference details, and propose likely matches, effectively lowering the technical barrier. Existing safeguards can be bypassed by breaking down the re-identification into benign tasks. I outline the attack at a high level, discuss implications for releasing rich qualitative data in the age of LLM agents, and propose mitigation recommendations and open problems. I have notified Anthropic of my findings.
Abstract (translated)
2025年12月4日，Anthropic发布了名为“Anthropic Interviewer”的AI工具，该工具可以大规模地运行定性访谈，并同时发布了一个包含1250份专业人士访谈的公共数据集（包括125名科学家），讨论他们使用AI进行研究的情况。特别关注于科学家这一子群体，我展示了市面上广泛可用的大规模语言模型（LLMs）结合网络搜索和代理功能可以将六分之一的二十四位受访者的访谈链接到具体科学作品上，并能够恢复关联作者，在某些情况下甚至能够唯一识别出受访者本人。 我的贡献在于展示现代基于LLM的代理使这种重新识别攻击变得简单且低投入：现成工具可以通过几个自然语言提示在网络上搜索、相互参考细节并提出可能匹配项，从而有效降低技术门槛。现有的安全措施可以被打破，通过将重新识别分解为看似无害的任务而绕过。 我概述了该攻击的高级别策略，并讨论在LLM代理时代发布丰富定性数据的影响，提出了缓解建议和开放问题。我已经通知了Anthropic我的发现。
URL
https://arxiv.org/abs/2601.05918
