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.

翻译：2025年12月4日，Anthropic发布了Anthropic Interviewer——一款用于大规模开展定性访谈的人工智能工具，同时公开了一个包含1250名专业人士访谈的数据集，其中涉及125名科学家关于其在研究中使用人工智能的讨论。聚焦于科学家子集，本文证明具备网络搜索与智能体能力的广泛可用大语言模型，能够将二十四份访谈中的六份与特定科学成果相关联，还原出对应作者，并在部分案例中唯一识别出受访者。本文的核心贡献在于揭示：基于现代大语言模型的智能体使得此类重新识别攻击变得简易且低耗时——现成工具仅需少量自然语言指令即可执行网络搜索、交叉比对细节并提出潜在匹配，从而显著降低了技术门槛。现有防护措施可通过将重新识别任务分解为多个良性子任务来规避。本文从宏观层面概述了攻击流程，探讨了在智能体时代发布丰富定性数据所引发的隐忧，并提出缓解建议与待解难题。研究团队已将相关发现通报Anthropic。