New research involving scientists from Anthropic and ETH Zurich suggests that modern artificial intelligence systems could identify the real-world identities behind supposedly anonymous internet accounts. The study, published as a preprint on arXiv, shows that large language models (LLMs) may be capable of analyzing online activity and linking pseudonymous profiles to real individuals at scale.

The research, titled Large-scale online deanonymization with LLMs, explores how AI agents can automate the process of deanonymization – the act of connecting anonymous or pseudonymous online accounts to real identities. Traditionally, this process required significant manual investigation by analysts who searched through posts, writing styles, and scattered online clues. However, the researchers demonstrate that modern AI models can perform many of these steps automatically.

In the study, the AI system analyzed public text from online platforms and extracted identity-related signals such as personal interests, demographic clues, writing style, and incidental details revealed in posts. The AI then searched for matching profiles across the web and evaluated whether the clues aligned with known individuals.

To test the method, researchers created several datasets with known ground-truth identities

One experiment attempted to match Hacker News users with their LinkedIn profiles, even after removing obvious identifiers such as names and usernames. Another dataset involved linking pseudonymous Reddit accounts across different communities. A third dataset split a single user’s posting history into two separate profiles to see if the AI could identify that they belonged to the same person.

The results showed that LLM-based systems significantly outperformed traditional deanonymization techniques. In some cases, the models achieved up to 68% recall with about 90% precision, meaning the AI correctly identified many accounts while maintaining relatively low error rates. Conventional methods in the same experiments achieved close to zero success.

Researchers say the findings highlight how AI can replicate tasks that once required hours of work by human investigators. An AI system can automatically extract identity-related features from text, search for potential matches among thousands of profiles, and reason about which candidate is most likely correct.

This development is significant because anonymity has long been considered a basic protection for many internet users

Pseudonymous accounts are widely used by journalists, whistleblowers, activists, and ordinary individuals who want to discuss sensitive topics without revealing their real identities.

The study suggests that this layer of protection – sometimes called “practical obscurity” – may be weakening as AI systems become better at connecting digital clues across platforms. If automated tools can perform this work quickly and cheaply, the barrier to identifying anonymous users could drop dramatically.

Researchers estimate that the cost of identifying an online account using their experimental pipeline could fall between $1 and $4 per profile, meaning large-scale investigations could be conducted relatively cheaply.

However, the authors also note that the research was conducted in controlled environments using public data. The paper has not yet been peer-reviewed, and the researchers intentionally withheld some technical details to reduce the risk of misuse.

Even so, the findings have already sparked debate among privacy experts and technologists

The work suggests that individuals may need to rethink how much personal information they reveal online – even in spaces that appear anonymous. Looking ahead, researchers say further work is needed to understand both the risks and possible defenses against AI-powered deanonymization. Potential solutions could include improved privacy tools, stronger platform safeguards, or AI systems designed to anonymize sensitive data before it is shared publicly.

As artificial intelligence becomes more capable at analyzing massive volumes of online content, the study highlights a growing challenge: balancing the power of AI-driven discovery with the need to protect personal privacy in the digital age.