LLMs Link Pseudonymous Profiles at Scale
New research finds Large Language Models (LLMs) can link pseudonymous accounts across platforms by mining unstructured text. With web access or a fixed candidate set, agents achieve up to 68% recall at 90% precision, far ahead of classical baselines. The work argues practical obscurity no longer protects users and urges updated threat models.
Pseudonymity on public forums has long relied on practical obscurity: you could be found, but only if someone cared enough to look. New work shows that Large Language Models (LLMs) turn that old, creaky lock into a revolving door. By lifting identifying signals out of ordinary posts and profiles, LLMs can link accounts at scale with unsettling competence.
The researchers test two attack modes. In one, an agent with web search converts a target’s posts into concrete claims and hunts for likely identities. On datasets from Hacker News and Reddit, this approach saw roughly 25 to 67 percent recall at 70 to 90 percent precision. On a vetted Hacker News set, it correctly identified 226 of 338 targets, about 67 percent recall at 90 percent precision. What a dedicated human might do in hours now fits into an automated loop.
The second mode is a closed-world pipeline that breaks deanonymisation into four steps: Extract, Search, Reason and Calibrate. The model first turns free-form text into identity-relevant micro-data, then uses embeddings to pull a shortlist of candidates, before applying LLM reasoning to verify matches and a final calibration to tune confidence. Across three settings it substantially outperforms classical baselines, reaching up to 68 percent recall at 90 percent precision, while non-LLM methods are near zero in some tasks. The components matter: dense retrieval often puts the true match in the top 15, but the Reason stage is the workhorse. In one comparison, adding Reason lifted recall at 99 percent precision from 4.4 percent to 45.1 percent.
To ground the numbers, the authors build three datasets with verifiable links. One connects Hacker News profiles to LinkedIn via cross-platform references in profiles. Another pairs users across Reddit movie forums. A third splits a single Redditor’s history in time to create two pseudonyms to be matched. They also report results on a set of interview transcripts. Performance degrades as the candidate pool grows, but the Reason-enabled pipeline holds up better and remains non-trivial.
If this sounds familiar, it is. The Netflix Prize episode showed how a few structured signals could pierce anonymity. The difference now is that LLMs work directly on raw prose. Style, topics, and those stray biographical crumbs we all drop become a composite signature. Yesterday’s manual feature engineering becomes today’s prompt.
For defenders, the security story is plain. Practical obscurity no longer protects at scale. Automated linking raises the stakes for surveillance, targeted social engineering, harassment and commercial profiling, and it erodes the safety of communities that depend on separation between handles and real identities. When the marginal cost of a search falls close to zero, threat models must change.
There are practical steps that follow from the evidence here. Platforms can reduce exposure through data minimisation, rate-limited and audited access to user content, and privacy-preserving processing such as differential privacy where feasible. Policies and monitoring need to assume adversaries will automate feature extraction, retrieval and verification, not just scrape.
The caveats are sensible. The datasets rely on ground-truthable links or synthetic splits, which may overestimate recall for harder, cleaner separations. Open-web agents depend on external search systems. The authors avoided targeting real pseudonymous users, did not release code or processed data, and false positives remain a risk as pools grow. Still, the trajectory is clear. Each time we make behaviour machine-readable, the shadows shorten. The rhyme with past de-anonymisation work is unmistakable; the verse has just become easier to sing at scale.
Additional analysis of the original ArXiv paper
