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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. Compared to prior deanonymization work (e.g., on the Netflix prize) that required structured data or manual feature engineering, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. 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.
There's no quality of an LLM that would make this possible. It's just more hallucinations and poor tool use.
Why not? if LLMs are good at predicting mean outcomes for the next symbol in a string, and humans have idiosyncrasies that deviate from that mean in a predictable way, I don't see why you couldn't detect and correlate certain language features that map to a specific user. You could use things like word choice, punctuation, slang, common misspellings, sentence structure... For example, I started with a contradicting question, I used "idiosyncrasies", I wrote "LLMs" without an apostrophe, "language features" is a term of art, as is "map" as a verb, etc. None of these are indicative on their own, but unless people are taking exceptional care to either hyper-normalize their style, or explicitly spiking their language with confounding elements, I don't see why an LLM wouldn't be useful for this kind of espionage.
I wonder if this will have a homogenizing effect on the anonymous web. It might become an accepted practice to communicate in a highly formalized style to make this kind of style fingerprinting harder.
It's a language model not a classification model. People have already tried a similar experiment to have LLMs detect if a LLM wrote text or not and it couldn't.
This is in some ways an easier problem than classifying LLM vs non-LLM authorship. That only has two possible outcomes, and it's pretty noisy because LLMs are trained to emulate the average human. Here, you can generate an agreement score based on language features per comment, and cluster the comments by how they disagree with the model. Comments that disagree in particular ways (never uses semicolons, claims to live in Canada, calls interlocutors "buddy", writes run-on sentences, etc.) would be clustered together more tightly. The more comments two profiles have in the same cluster(s), the more confident the match becomes. I'm not saying this attack is novel or couldn't be accomplished without an LLM, but it seems like a good fit for what LLMs actually do.