Privacy

We show that large language models can be used to perform at-scale deanonymization.

With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting.

Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to:
- 1. extract identity-relevant features,
- 2. search for candidate matches via semantic embeddings, and
- 3. reason over top candidates to verify matches and reduce false positives...

Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method.

Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered...

The following prompt is used...

Source: https://arxiv.org/pdf/2602.16800 [2026-02-18]

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I don’t know why people are so keen to put the details of their private life in public; they forget that invisibility is a superpower...
~ Banksy

Related: https://cloudflare.com/learning/ai/data-poisoning