# UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification

arXiv: https://arxiv.org/abs/2410.12318 · HTML: https://mlsanity.com/paper/2410.12318/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [LLM360: Towards Fully Transparent Open-Source LLMs](https://mlsanity.com/paper/2312.06550/) (2023)
- [Watermarking Pre-trained Language Models with Backdooring](https://mlsanity.com/paper/2210.07543/) (2022)
- [Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique](https://mlsanity.com/paper/2407.10887/) (2024)

## Built on by (most cited first, 18 of 18)

- [Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends](https://mlsanity.com/paper/2508.11548/) (2025)
- [SoK: Large Language Model Copyright Auditing via Fingerprinting](https://mlsanity.com/paper/2508.19843/) (2025)
- [Scalable Fingerprinting of Large Language Models](https://mlsanity.com/paper/2502.07760/) (2025)
- [CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor](https://mlsanity.com/paper/2509.09703/) (2025)
- [MEraser: An Effective Fingerprint Erasure Approach for Large Language Models](https://mlsanity.com/paper/2506.12551/) (2025)
- [Are Robust LLM Fingerprints Adversarially Robust?](https://mlsanity.com/paper/2509.26598/) (2025)
- [Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://mlsanity.com/paper/2510.18333/) (2025)
- [iSeal: Encrypted Fingerprinting for Reliable LLM Ownership Verification](https://mlsanity.com/paper/2511.08905/) (2025)
- [AttnDiff: Attention-based Differential Fingerprinting for Large Language Models](https://mlsanity.com/paper/2604.05502/) (2026)
- [FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing](https://mlsanity.com/paper/2508.02092/) (2025)
- [From Construction to Injection: Edit-Based Fingerprints for Large Language Models](https://mlsanity.com/paper/2509.03122/) (2025)
- [Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation](https://mlsanity.com/paper/2605.18474/) (2026)
- [Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models](https://mlsanity.com/paper/2601.04261/) (2026)
- [ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models](https://mlsanity.com/paper/2601.08189/) (2026)
- [Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways](https://mlsanity.com/paper/2604.21083/) (2026)
- [Construction-Driven Injection: Linguistically-Grounded Edit-Based Code-Mixing Fingerprints for Large Language Models](https://mlsanity.com/paper/2607.25633/) (2026)
- [DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protection](https://mlsanity.com/paper/2601.08223/) (2026)
- [System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer](https://mlsanity.com/paper/2610.00253/) (2026)

## Abstract (arXiv)

Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access. In this paper, we introduce UTF, a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Under-trained tokens are tokens that the model has not fully learned during its training phase. By utilizing these tokens, we perform supervised fine-tuning to embed specific input-output pairs into the model. This process allows the LLM to produce predetermined outputs when presented with certain inputs, effectively embedding a unique fingerprint. Our method has minimal overhead and impact on model's performance, and does not require white-box access to target model's ownership identification. Compared to existing fingerprinting methods, UTF is also more effective and robust to fine-tuning and random guess.