Researchers led by Simon Lermen and collaborators from MATS Research, ETH Zurich, and Anthropic publish a pre-press paper demonstrating that large language models can deanonymize internet users by linking pseudonymous posts to real profiles across Hacker News, Reddit, LinkedIn, and interview transcripts. In experiments the method identified 226 of 338 Hacker News targets (67% recall) at 90% precision, costing about $2,000 total; the authors warn this enables scalable, affordable deanonymization.
Key Points
- 1Demonstrate automated deanonymization: LLM agents identified 226 of 338 users (67% recall).
- 2Show that LLMs extract identity signals from unstructured text, enabling scalable profile linking.
- 3Imply cheap abuse: full experiment cost ~$2,000, meaning adversaries can deanonymize at low cost.
Scoring Rationale
High practical impact and methodological novelty, tempered by preprint status and limited public replication or code availability.
Sources
Public references used for this report.
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