LLMs Enable Large-Scale Deanonymisation Of Users
Researchers at ETH Zurich, the ML Alignment Theory Scholars program, and Anthropic posted an arXiv preprint describing a pipeline that uses commercial LLM APIs to deanonymise pseudonymous online accounts for as little as $1.41 per target. The ESRC (Extract, Search, Reason, Calibrate) method achieved 45.1% recall at 99% precision linking Hacker News to LinkedIn across 89,000 users and projects roughly 35% recall at 90% precision at million-scale, highlighting platform privacy risks.
Key Points
- 1Demonstrates ESRC pipeline deanonymises pseudonymous users at scale using commercial LLM APIs for $1.41 per target.
- 2Shows substantial accuracy gains, achieving 45.1% recall at 99% precision against an 89,000-user candidate pool.
- 3Warns that current API guardrails fail, so platforms must deploy rate limits and scraping detection.
Scoring Rationale
High novelty, broad scope and direct exploitability drive score; constrained by preprint status and absence of released pipeline code.
Sources
Public references used for this report.
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