For years, we have thought that our online nicknames will keep us safe. We considered that the verification of identities would be too costly. But recent research indicates that this situation is being dramatically shifted by large language models. Starting from unstructured text (anonymous forum posts, comment histories, and even brief interview transcripts) LLM-based systems can now match users to their real identities in minutes and for just a few dollars, automating what would take a skilled investigator hours of manual OSINT work.

In this talk, we will be looking at the deanonymization framework as introduced in "Large-scale online deanonymization with LLMs" by ETH Zurich (2026) . We will discuss how successful AI companies and governments would be at finding the person behind the "nickname", if they decided to leverage this capability for user surveillance In a world where hiding behind aliases is no longer enough, we will address how we, as the free software community, need to rethink online privacy.