Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks

Challenge:	Copyright Detective is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs.
Approach:	They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing .
Outcome:	The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs.

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LLMs and Copyright Risks: Benchmarks and Mitigation Approaches (2025.naacl-tutorial)

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Denghui Zhang, Zhaozhuo Xu, Weijie Zhao

Semantic Text Processing

Challenge:	Large Language Models (LLMs) have revolutionized natural language processing, but their widespread use has raised significant copyright concerns.
Approach:	This tutorial will provide an overview of relevant copyright principles and their application to AI and examine specific copyright issues in LLM development and deployment.
Outcome:	The course will provide an overview of relevant copyright principles and their application to AI, followed by an examination of specific copyright issues in LLM development and deployment.

Certified Mitigation of Worst-Case LLM Copyright Infringement (2025.emnlp-main)

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Jingyu Zhang, Jiacan Yu, Marc Marone, Benjamin Van Durme, Daniel Khashabi

Language Models

Challenge:	Large language models are trained on vast datasets that include copyrighted material or content with usage restrictions.
Approach:	They propose a "copyright takedown" method that provides certified copyright take down . they use a combination of inference-time and rewriting techniques to transform potentially infringing segments .
Outcome:	The proposed method reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention.

Profiling LLM’s Copyright Infringement Risks under Adversarial Persuasive Prompting (2025.findings-emnlp)

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Jikai Long, Ming Liu, Xiusi Chen, Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang

Responsible & Trustworthy NLP

Challenge:	Large Language Models have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement.
Approach:	They propose a structured persuasion workflow to analyze the influence of persuasive prompts on LLM outputs.
Outcome:	The proposed method analyzes the influence of persuasive prompts on LLM outputs.

Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024.lrec-main)

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Abhinav Sukumar Rao, Atharva Roshan Naik, Sachin Vashistha, Somak Aditya, Monojit Choudhury

Responsible & Trustworthy NLP

Challenge:	Existing methods to jailbreak large language models have been poorly studied . a recent study showed that non-expert users can jailbreak LLMs by manipulating their prompts .
Approach:	They propose a formalism and a taxonomy of known (and possible) jailbreaks . they propose generating a dataset of model outputs across 3700 jailbreak prompts a 'prompt' attack is a new attack popularly categorized as "prompting injection attacks"
Outcome:	The proposed model exploits 3700 jailbreak prompts over 4 tasks to analyze their effectiveness . authors show that the model can learn to perform a new task on unseen examples .

Do LLMs Know to Respect Copyright Notice? (2024.emnlp-main)

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Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang

Language Models

Challenge:	Existing studies have focused on the occurrence of copyright violations in LLM output, but a negative answer would suggest that LLMs will become the primary facilitator and accelerator of copy right infringement behavior.
Approach:	They propose to examine whether LLMs respect copyright information in user input . they use a set of language models, user prompts, and copyrighted materials .
Outcome:	The proposed model will be the primary facilitator and accelerator of copyright infringement behavior, the study finds . the study also provides a benchmark dataset serving as a test bed for evaluating infringement behaviors by LLMs .

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation (2024.emnlp-main)

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Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao

Robustness in NLP

Challenge:	Large Language Models (LLMs) have transformed machine learning but have raised significant legal concerns due to their potential to produce text that infringes on copyrights.
Approach:	They propose a lightweight, real-time defense mechanism to prevent the generation of copyrighted text by evaluating methods and testing attack strategies.
Outcome:	The proposed defense significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests.

LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text (2024.eacl-long)

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Dor Bernsohn, Gil Semo, Yaron Vazana, Gila Hayat, Ben Hagag, Joel Niklaus, Rohit Saha, Kyryl Truskovskyi

Challenge:	a recent study focused on detecting legal violations within unstructured textual data . a similar study focused only on associating violations with potentially affected individuals .
Approach:	They constructed two datasets using Large Language Models (LLMs) they publicize the results to advance legal natural language processing research .
Outcome:	The proposed datasets and the code used for the experiments have been released to advance legal natural language processing (NLP)

Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)

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Muhao Chen, Chaowei Xiao, Huan Sun, Lei Li, Leon Derczynski, Anima Anandkumar, Fei Wang

Responsible & Trustworthy NLP

Challenge:	a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs .
Approach:	This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs .
Outcome:	This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs .

Where Am I From? Identifying Origin of LLM-generated Content (2024.emnlp-main)

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Liying Li, Yihan Bai, Minhao Cheng

Challenge:	Generative models have produced high-quality content, but they pose security risks . a new framework for deep learning systems enables the tracing of AI-generated content back to its source .
Approach:	They propose a digital forensics framework that embeds a secret watermark into the generated output and a "depth watermark" this watermark strengthens the link between content and generator, enabling accurate tracing while maintaining the quality of the generated content.
Outcome:	The proposed framework ensures accurate tracing while maintaining quality of generated content.

PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)

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Jooyoung Lee, Toshini Agrawal, Adaku Uchendu, Thai Le, Jinghui Chen, Dongwon Lee

Text Generation

Challenge:	Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection.
Approach:	They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization.
Outcome:	The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo.