RAP-SM: Robust Adversarial Prompt via Shadow Models for Copyright Verification of Large Language Models

• URL: http://arxiv.org/abs/2505.06304v1
• Date: Thu, 08 May 2025 03:21:58 GMT
• Title: RAP-SM: Robust Adversarial Prompt via Shadow Models for Copyright Verification of Large Language Models
• Authors: Zhenhua Xu, Zhebo Wang, Maike Li, Wenpeng Xing, Chunqiang Hu, Chen Zhi, Meng Han,
• Abstract summary: RAP-SM is a novel framework that extracts a public fingerprint for an entire series of large language models.<n> Experimental results demonstrate that RAP-SM effectively captures the intrinsic commonalities among different models.
• Score: 12.459241957411669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have underscored the importance of safeguarding intellectual property rights through robust fingerprinting techniques. Traditional fingerprint verification approaches typically focus on a single model, seeking to improve the robustness of its fingerprint.However, these single-model methods often struggle to capture intrinsic commonalities across multiple related models. In this paper, we propose RAP-SM (Robust Adversarial Prompt via Shadow Models), a novel framework that extracts a public fingerprint for an entire series of LLMs. Experimental results demonstrate that RAP-SM effectively captures the intrinsic commonalities among different models while exhibiting strong adversarial robustness. Our findings suggest that RAP-SM presents a valuable avenue for scalable fingerprint verification, offering enhanced protection against potential model breaches in the era of increasingly prevalent LLMs.

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