Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits. The legal landscape is struggling to keep pace with these rapid advancements, with ongoing debates about whether generated text might plagiarize copyrighted materials. Current LLMs may infringe on copyrights or overly restrict non-copyrighted texts, leading to these challenges: (i) the need for a comprehensive evaluation benchmark to assess copyright compliance from multiple aspects; (ii) evaluating robustness against safeguard bypassing attacks; and (iii) developing effective defense targeted against the generation of copyrighted text. To tackle these challenges, we introduce a curated dataset to evaluate methods, test attack strategies, and propose lightweight, real-time defense to prevent the generation of copyrighted text, ensuring the safe and lawful use of LLMs. Our experiments demonstrate that current LLMs frequently output copyrighted text, and that jailbreaking attacks can significantly increase the volume of copyrighted output. Our proposed defense mechanism significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests. Code is publicly available at https://github.com/xz-liu/SHIELD
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
Pine AI；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）；University of Michigan（密歇根大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Johns Hopkins University（约翰霍普金斯大学）；University of Notre Dame（圣母大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
City University of Hong Kong（香港城市大学）；Microsoft（微软）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google LLC（谷歌公司）；Arizona State University（亚利桑那州立大学）；Stevens Institute of Technology（史蒂文斯理工学院）
Forgetting-MarI: LLM Unlearning via Marginal Information Regularization
Department of Mathematics, University of California Davis, USA（加州大学戴维斯分校数学系）；SLAC National Accelerator Laboratory, Stanford University, USA（斯坦福大学SLAC国家加速器实验室）；Department of Computer Science, University of California Davis, USA（加州大学戴维斯分校计算机科学系）
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Pittsburgh（匹兹堡大学）；University of Arizona（亚利桑那大学）；University of Central Florida（中佛罗里达大学）
GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；UC Santa Cruz（加州大学圣克鲁兹分校）；UIUC（伊利诺伊大学香槟分校）；Southeast University（东南大学）；BIAI, ZJUT（脑科学与类脑智能研究院，浙江大学）；D5Data.ai
