# Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation

arXiv: https://arxiv.org/abs/2605.18474 · HTML: https://mlsanity.com/paper/2605.18474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Text-to-LoRA: Instant Transformer Adaption](https://mlsanity.com/paper/2506.06105/) (2025)
- [Watermarking Pre-trained Language Models with Backdooring](https://mlsanity.com/paper/2210.07543/) (2022)
- [One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models](https://mlsanity.com/paper/2406.05491/) (2024)
- [Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique](https://mlsanity.com/paper/2407.10887/) (2024)
- [TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification](https://mlsanity.com/paper/2402.12991/) (2024)
- [UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification](https://mlsanity.com/paper/2410.12318/) (2024)
- [Recurrent Diffusion for Large-Scale Parameter Generation](https://mlsanity.com/paper/2501.11587/) (2025)
- [Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalization](https://mlsanity.com/paper/2405.14132/) (2024)
- [Fingerprint Vector: Enabling Scalable and Efficient Model Fingerprint Transfer via Vector Addition](https://mlsanity.com/paper/2409.08846/) (2024)
- [Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs](https://mlsanity.com/paper/2505.17601/) (2025)
- [The Challenge of Identifying the Origin of Black-Box Large Language Models](https://mlsanity.com/paper/2503.04332/) (2025)
- [EditMark: Watermarking Large Language Models based on Model Editing](https://mlsanity.com/paper/2510.16367/) (2025)
- [iSeal: Encrypted Fingerprinting for Reliable LLM Ownership Verification](https://mlsanity.com/paper/2511.08905/) (2025)

## Built on by (most cited first, 2 of 2)

- [Your "Pro" LLM Subscription May Actually Be "Free": Exposing Fingerprint Spoofing Risks in LLM Inference Services](https://mlsanity.com/paper/2606.16100/) (2026)
- [Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling](https://mlsanity.com/paper/2609.22145/) (2026)

## Abstract (arXiv)

The widespread deployment and redistribution of large language models (LLMs) have made model provenance tracking a critical challenge. While existing LLM fingerprinting methods, particularly active approaches that embed identity signals via fine-tuning, achieve high accuracy and robustness, they suffer from significant scalability bottlenecks. These methods typically treat fingerprint injection as an independent, one-off optimization task rather than a reusable capability, necessitating separate, resource-intensive training for every new identity. This incurs prohibitive computational costs and deployment delays. To address this, we propose Prompt2Fingerprint (P2F), the first framework that reformulates fingerprinting as a conditional parameter generation task. By leveraging a specialized generator, P2F maps textual descriptions directly to low-rank parameter increments in a single forward pass, enabling plug-and-play LLM fingerprint injection without further model retraining. Our experiments demonstrate that P2F maintains high fingerprint accuracy, harmlessness, and robustness while significantly reducing computational overhead, offering a scalable and instant solution for LLM ownership management.