# Instructional Fingerprinting of Large Language Models

arXiv: https://arxiv.org/abs/2401.12255 · HTML: https://mlsanity.com/paper/2401.12255/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 78 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training](https://mlsanity.com/paper/2401.05566/) (2024)

## Built on by (most cited first, 30 of 68)

- [Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities](https://mlsanity.com/paper/2408.07666/) (2024)
- [Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique](https://mlsanity.com/paper/2407.10887/) (2024)
- [REEF: Representation Encoding Fingerprints for Large Language Models](https://mlsanity.com/paper/2410.14273/) (2024)
- [We Should Chart an Atlas of All the World's Models](https://mlsanity.com/paper/2503.10633/) (2025)
- [Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging](https://mlsanity.com/paper/2404.05188/) (2024)
- [Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking](https://mlsanity.com/paper/2503.04636/) (2025)
- [Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test](https://mlsanity.com/paper/2506.06975/) (2025)
- [ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models](https://mlsanity.com/paper/2405.02466/) (2024)
- [Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges](https://mlsanity.com/paper/2409.19993/) (2024)
- [Model Provenance Testing for Large Language Models](https://mlsanity.com/paper/2502.00706/) (2025)
- [Scalable Fingerprinting of Large Language Models](https://mlsanity.com/paper/2502.07760/) (2025)
- [DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation](https://mlsanity.com/paper/2505.19504/) (2025)
- [Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification](https://mlsanity.com/paper/2506.01631/) (2025)
- [The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources](https://mlsanity.com/paper/2406.16746/) (2024)
- [Blackbox Model Provenance via Palimpsestic Membership Inference](https://mlsanity.com/paper/2510.19796/) (2025)
- [SOS! Soft Prompt Attack Against Open-Source Large Language Models](https://mlsanity.com/paper/2407.03160/) (2024)
- [Independence Tests for Language Models](https://mlsanity.com/paper/2502.12292/) (2025)
- [SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From](https://mlsanity.com/paper/2509.26404/) (2025)
- [LLM Fingerprinting via Semantically Conditioned Watermarks](https://mlsanity.com/paper/2505.16723/) (2025)
- [Watermarking Language Models through Language Models](https://mlsanity.com/paper/2411.05091/) (2024)
- [Riemannian-Geometric Fingerprints of Generative Models](https://mlsanity.com/paper/2506.22802/) (2025)
- [Are Robust LLM Fingerprints Adversarially Robust?](https://mlsanity.com/paper/2509.26598/) (2025)
- [On Protecting Agentic Systems' Intellectual Property via Watermarking](https://mlsanity.com/paper/2602.08401/) (2026)
- [LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis](https://mlsanity.com/paper/2502.20589/) (2025)
- [ImF: Implicit Fingerprint for Large Language Models](https://mlsanity.com/paper/2503.21805/) (2025)
- [CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models](https://mlsanity.com/paper/2505.16785/) (2025)
- [Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://mlsanity.com/paper/2510.18333/) (2025)
- [SVIP: Towards Verifiable Inference of Open-source Large Language Models](https://mlsanity.com/paper/2410.22307/) (2024)
- [Invisible Traces: Using Hybrid Fingerprinting to identify underlying LLMs in GenAI Apps](https://mlsanity.com/paper/2501.18712/) (2025)
- [The Challenge of Identifying the Origin of Black-Box Large Language Models](https://mlsanity.com/paper/2503.04332/) (2025)

## Abstract (arXiv)

The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (e.g. restricting commercial use). In this study, we present a pilot study on LLM fingerprinting as a form of very lightweight instruction tuning. Model publisher specifies a confidential private key and implants it as an instruction backdoor that causes the LLM to generate specific text when the key is present. Results on 11 popularly-used LLMs showed that this approach is lightweight and does not affect the normal behavior of the model. It also prevents publisher overclaim, maintains robustness against fingerprint guessing and parameter-efficient training, and supports multi-stage fingerprinting akin to MIT License. Code is available in https://cnut1648.github.io/Model-Fingerprint/.