[2607.10252] One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions

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Computer Science > Cryptography and Security

arXiv:2607.10252 (cs)

[Submitted on 11 Jul 2026]

Title:One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions

Authors:Tomas Bruckner<br>View a PDF of the paper titled One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions, by Tomas Bruckner

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Abstract:Large language models (LLMs) are increasingly consumed through opaque serving chains - API aggregators, resellers, and inference providers - in which the client has no technical means to confirm that the model answering is the model advertised, and recent audits show that a substantial fraction of commercial endpoints deviate from the vendor's reference weights. Existing identification techniques require long generated texts, token-level log-probabilities, adversarially crafted prompts, or the model owner's cooperation. We show that far weaker evidence suffices. We define a behavioral fingerprint of an LLM as the empirical distribution of its answers to trivial one-word prompts - "name a random number between 1 and 100" - collected across four languages at a cost of one output token per query. Measuring 165 models served via a large commercial aggregator (OpenRouter), we find that (i) these distributions are highly non-uniform (median cell entropy 1.0 bit) and model-specific: split halves of the same model's samples lie an order of magnitude closer than samples of different models; (ii) Jensen-Shannon divergence between fingerprints recovers model lineage, assigning a model to its documented family with 59.5% leave-one-out accuracy against an 18.4% chance rate; and (iii) a biometric-style verification protocol achieves a 7.3% equal error rate with the full 40-cell battery, and below 11% with eight probe cells - roughly a hundred single-token queries per audit. We further report ecosystem anomalies, including a proprietary-branded flagship endpoint distributionally indistinguishable from an open-weight Qwen model. The protocol, prompts, raw data, and analysis code are released for reproduction and operational use.

Subjects:

Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as:<br>arXiv:2607.10252 [cs.CR]

(or<br>arXiv:2607.10252v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.10252

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Tomas Bruckner [view email]<br>[v1]<br>Sat, 11 Jul 2026 10:34:17 UTC (143 KB)

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