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

• URL: http://arxiv.org/abs/2605.18474v2
• Date: Tue, 19 May 2026 08:19:47 GMT
• Title: Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
• Abstract summary: We propose Prompt2Fingerprint, a framework that reformulates fingerprinting as a conditional parameter generation task.<n>By leveraging a specialized generator, P2F maps textual descriptions directly to low-rank parameter increments in a single forward pass.<n>Our experiments demonstrate that P2F maintains high fingerprint accuracy, harmlessness, and robustness while significantly reducing computational overhead.
• Score: 46.76055970078189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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