by Tyler Durden
February 27, 2026
from ZeroHedge Website

It looks like A.I. can now unmask any anonymous account on the Internet.

That's according to a new study by,

Simon Lermen (MATS), Daniel Paleka (ETH Zurich), Joshua Swanson (ETH Zurich), Michael Aerni (ETH Zurich), Nicholas Carlini (Anthropic), and Florian Tramèr (ETH Zurich),

...published on arXiv.

In the paper, "Large-Scale Online Deanonymization with LLMs," the researchers show that modern large language models (LLMs) can re-identify people behind pseudonymous online accounts at a scale and accuracy that far surpass previous techniques.

The core contribution is an automated deanonymization pipeline powered by LLMs, according to the new study. Instead of relying on structured datasets or hand-engineered features - like earlier attacks on the Netflix Prize dataset - the system works directly on raw, unstructured text.

Given posts, comments, or interview transcripts written under a pseudonym, the pipeline extracts identity-relevant signals, searches for likely matches using semantic embeddings, and then uses higher-level reasoning to verify the most promising candidates while filtering out false positives.

The result is a scalable attack that mirrors - and in some cases exceeds - the effectiveness of a dedicated human investigator.

To evaluate their approach, the researchers constructed three datasets with known ground truth.

The first links pseudonymous Hacker News users to real-world LinkedIn profiles, relying on cross-platform clues embedded in public text.

 

The second matches users across movie discussion communities on Reddit.

 

The third takes a single Reddit user's history, splits it into two time-separated profiles, and tests whether the system can reconnect them.

Across all three settings, LLM-based methods dramatically outperformed classical baselines, which often achieved near-zero recall.

The headline numbers are striking. In some experiments, the system achieved up to 68% recall at 90% precision - meaning it correctly identified a substantial portion of targets while keeping false accusations low.

Even when matching temporally split Reddit accounts separated by a year, performance remained strong. In contrast, traditional non-LLM approaches struggled to produce meaningful matches.

The findings suggest that advances in reasoning and representation learning have transformed deanonymization from a niche, data-hungry attack into a broadly applicable capability.

 

The study says that a key concern is that the attack pipeline is composed of individually benign steps:

• summarizing text

• generating

• ranking candidates

• reasoning over matches

No single component appears inherently malicious, making it difficult to detect or restrict through conventional safeguards.

Moreover, the study finds that increasing model reasoning effort improves deanonymization performance, implying that as frontier models become more capable, the attack may become even more effective by default.

The broader implication is that "practical obscurity" - the idea that scattered, pseudonymous posts are safe because linking them is too labor-intensive - may no longer hold.

Persistent usernames, writing style, niche interests, and cross-platform references can collectively act as a fingerprint. The authors conclude that threat models for online privacy need to be reconsidered in light of LLM capabilities.

While not every account can be unmasked, and performance varies by context, the study makes clear that the technical barrier to large-scale deanonymization has fallen dramatically.