# Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Dataset

arXiv: https://arxiv.org/abs/2601.05918 · HTML: https://mlsanity.com/paper/2601.05918/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 2 of 2)

- [Position: Privacy Is Not Just Memorization!](https://mlsanity.com/paper/2510.01645/) (2025)
- [Developing Story: Case Studies of Generative AI's Use in Journalism](https://mlsanity.com/paper/2406.13706/) (2024)

## Built on by (most cited first, 7 of 7)

- [From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents](https://mlsanity.com/paper/2603.18382/) (2026)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [Boundary-targeted Membership Inference Attacks on Safety Classifiers](https://mlsanity.com/paper/2605.22373/) (2026)
- [LLM Anonymization Against Agentic Re-Identification](https://mlsanity.com/paper/2605.30848/) (2026)
- [AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews](https://mlsanity.com/paper/2606.20588/) (2026)
- [Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy](https://mlsanity.com/paper/2606.27936/) (2026)
- [AgentDoxx: Agentic Re-identification of Anonymized Text with Web Search](https://mlsanity.com/paper/2610.05586/) (2026)

## Abstract (arXiv)

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.