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

TL;DR

Prompt2Fingerprint introduces a novel, plug-and-play framework that efficiently generates model fingerprints from text descriptions, eliminating the need for resource-intensive retraining and enabling scalable LLM ownership verification.

Contribution

It reformulates LLM fingerprinting as a conditional parameter generation task, allowing instant, reusable fingerprint injection without additional training.

Findings

Maintains high fingerprint accuracy and robustness

Reduces computational overhead significantly

Enables scalable, instant fingerprinting for LLMs

Abstract

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.