A team has constructed an artificial intelligence apparatus capable of converting anonymous online communications into actual identities, linking pseudonymous Hacker News user accounts to LinkedIn profiles with 90% accuracy. The method, described in a study called “Large-scale online deanonymization with LLMs,” depends solely on web searches, text embeddings, and reasoning models like GPT-5. Each individual search incurs an expense of approximately $1 to $4 in AI subscription charges.
The Four Steps of the Attack
The system breaks into four steps: Extract, Search, Reason, and Calibrate. Extract feeds an AI someone’s posts and asks it to summarize the person — where they might live, their job, their hobbies, even a quirk like British spelling or a dog’s name. Search turns that summary into an embedding, a string of numbers used to find similar profiles among thousands of candidates. Reason compares the best few matches, checking details the way a human investigator would. Calibrate rates the AI’s own confidence, so it only guesses when it’s fairly sure.
A test involved collecting data from 338 Hacker News users who had linked their LinkedIn profile in their bio, removing any personally identifying information, before asking an AI agent to guess who they were using only web search. The agent correctly named 226 of them, 67%, and was wrong on roughly one in 10 guesses it made. In another test, the AI correctly identified at least 9 people from transcripts of Anthropic’s own interview research with 125 scientists, based solely on how they described their work.
Why It Matters
The researchers say the attack is difficult to stop because there is no single “deanonymize this person” switch that can be turned off. Every individual step appears innocent on its own. Crypto users are already familiar with this worry, pointing to the wave of doxxings and kidnapping attempts that followed the 2025 Coinbase data breach.
| Metric | Value |
|---|---|
| Correct guesses (total) | 226 out of 338 |
| Accuracy on 338 users | 67% |
| Correct guesses (scientists) | At least 9 out of 125 |
| Correct guesses (precision) | 90% |
From a field of 89,000 candidates, the best AI method still managed to catch roughly half of the true matches at 90% precision. Precision is the measure of how often its guesses turned out to be correct, while recall tracks how many real matches it actually found. The study’s most alarming figures are tempered by scale: the larger the haystack of possible candidates grows, the harder the needle becomes to locate.
What the Paper Withheld
Before publication, the research was cleared by ETH Zurich’s ethics review board. The team from ETH Zurich, MATS, and Anthropic kept back their code, prompts, and every real identity they found.
The authors did not release their tools or prompts, nor did they disclose any of the actual names they found. Instead, they recorded proof of a power that they claim already resides within today’s AI systems, even without a published paper.
The paper documents a capability that already exists, yet limited by the sheer scale of its search space. As the volume of data grows, so too does the difficulty of locating what is being sought within it.
The study’s main point is that the ability is there, yet limited by size. That is the weight of its conclusion, and it deserves consideration on its own terms.
Source material: “AI Can Now Doxx Your Anonymous Accounts? Here's What’s Going On,” Decrypt.
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