✳flâneur — a map of the web's best reading
Large-Scale Online Deanonymization with LLMs
simonlermen.substack.com · 1,111 words · saved by 1 readers
We measure the capabilities of LLMs to deanonymize users online.
Large-Scale Online Deanonymization with LLMs We measure the capabilities of LLMs to deanonymize users online. Simon Lermen Feb 24, 2026 52 5 8 Share TL;DR: We show that LLM agents can figure out who you are from your anonymous online posts. Across Hacker News, Reddit, LinkedIn, and anonymized interview transcripts, our method identifies users with high precision – and scales to tens of thousands of candidates. While it has been known that individuals can be uniquely identified by surprisingly few attributes, this was often practically limited. Data is often only available in unstructured form
Explore this link on the map →saved by
related reading
- [2602.16800] Large-scale online deanonymization with LLMsarxiv.org
- Guardian Angels: LLM Personalization for Productivity and Security · Gwern.netgwern.net
- 2025: The year in LLMssimonwillison.net
- Quantifying Truesight With SAEs · Gwern.netgwern.net
- [2606.06614] Re-Centering Humans in LLM Personalizationarxiv.org
- [2401.05566] Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingarxiv.org
- The lethal trifecta for AI agents: private data, untrusted content, and external communicationsimonwillison.net
- The case for more ambitious language model evals — LessWronglesswrong.com
- DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencodersweb.archive.org
- llm-security/README.md at main · greshake/llm-security · GitHubgithub.com
- GitHub - daviddao/awful-ai: 😈Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness · GitHubgithub.com
- Clio: Privacy-Preserving Insights into Real-World AI Usearxiv.org