One Token Is Enough: Fingerprinting LLMs from Single-Token Output Distributions

Jul 14, 2026

Researchers have developed a method to fingerprint large language models (LLMs) by analyzing the distribution of their responses to simple, single-token prompts such as "name a random number between 1 and 100." By testing 165 models via the OpenRouter aggregator, the method achieved 59.5% accuracy in identifying model lineage and a 7.3% equal error rate in verification, all using only single-token queries. The approach also uncovered cases where a proprietary model endpoint was distributionally indistinguishable from an open-weight Qwen model.

Why it matters: This technique provides a practical way for clients to verify which LLM is actually serving them through opaque API chains, helping address trust and transparency issues in commercial model deployment.

Full story at: arXiv Cryptography and Security ↗