One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions
Tomas Bruckner
cs.CR, cs.CL, cs.LG
Submitted: 2026-07-11
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs
- Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test
- How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
- A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks
- Deterministic or probabilistic? The psychology of LLMs as random number generators
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills
- Token Composition: A Graph Based on EVM Logs