Evidencing LLM Misuse: A Hands-on Forensic Tutorial on Copyright Infringement and Plagiarism Detection
TL;DR
This tutorial presents a unified forensic perspective on LLM content misuse, combining hands-on copyright-risk auditing with a flexible plagiarism module, and equips attendees with practical and conceptual tools for auditing black-box models, interpreting similarity evidence, and understanding content misuse beyond simple text matching.
Abstract
Large Language Models (LLMs) introduce serious risks of content misuse, spanning copyright infringement in the legal domain and plagiarism in the ethical and academic domain. Although prior work has studied these risks, researchers and practitioners still need practical ways to audit, interpret, and evidence them. This tutorial presents a unified forensic perspective on LLM content misuse. First, we introduce Copyright Detective, an interactive forensic system for detecting, analyzing, and visualizing potential copyright leakage in LLM outputs. Participants will learn how inference-time scaling reveals sporadic memorization under output uncertainty, and how persuasive jailbreak probing can serve as defensive red teaming for examining alignment-suppressed leakage. Second, we introduce LLM Plagiarism Detection, covering verbatim copying, paraphrased reuse, and idea-level appropriation. We will discuss, and where appropriate demonstrate, how candidate source retrieval and text alignment support plagiarism analysis, while highlighting factors such as model size, decoding strategies, and fine-tuning corpus similarity. By combining hands-on copyright-risk auditing with a flexible plagiarism module, this tutorial equips attendees with practical and conceptual tools for auditing black-box models, interpreting similarity evidence, and understanding content misuse beyond simple text matching.