← Back to Library

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

Authors: Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi Ma, Yun Huang, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang

Published: 2026-02-05

arXiv ID: 2602.05252v1

Added to Library: 2026-02-06 03:00 UTC

📄 Abstract

We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyright infringement versus compliance as an evidence discovery process rather than a static classification task due to the complex nature of copyright law. It integrates multiple detection paradigms, including content recall testing, paraphrase-level similarity analysis, persuasive jailbreak probing, and unlearning verification, within a unified and extensible framework. Through interactive prompting, response collection, and iterative workflows, our system enables systematic auditing of verbatim memorization and paraphrase-level leakage, supporting responsible deployment and transparent evaluation of LLM copyright risks even with black-box access.

🤖 AI Analysis

AI analysis is not available for this paper. This may be because the paper was not deemed relevant for AI security topics, or the analysis failed during processing.

📚 Read the Full Paper