用文本生成模型指纹，无需重训练即可快速植入身份标识。
Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
- 将指纹注入转化为文本到参数的条件生成任务
- 单次前向传播生成指纹，准确率高且抗干扰能力强
- 适合需要快速部署和批量管理模型版权的场景
大规模语言模型（LLMs）的广泛部署与分发使模型溯源成为关键挑战。现有主动指纹方法通过微调嵌入身份信号，虽具高精度与鲁棒性，但存在显著可扩展性瓶颈：通常将指纹注入视为独立、一次性优化任务，需为每个新身份单独进行资源密集型训练，导致计算成本高昂且部署延迟大。为此，我们提出 Prompt2Fingerprint（P2F），首个将指纹注入重构为条件参数生成任务的框架。通过专用生成器，P2F在单次前向传播中将文本描述映射为低秩参数增量，实现无需再训练的即插即用指纹注入。实验表明，P2F在保持高指纹准确性、无害性及鲁棒性的前提下，显著降低计算开销，为模型所有权管理提供高效即时的解决方案。
原文摘要 · Abstract (English)
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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