ISACL: Internal State Analyzer for Copyrighted Training Data Leakage

2025-08-25

AI Summary

Large language models (LLMs) may inadvertently memorize and reproduce copyrighted training data, posing significant copyright infringement risks; conventional post-hoc detection methods fail to prevent such sensitive content leakage proactively. This paper introduces the first pre-generation copyright leakage risk mitigation framework: it extracts intermediate-layer hidden state features from LLMs and trains a lightweight neural classifier to predict potential copyright violation risks in real time; this classifier is tightly coupled with retrieval-augmented generation (RAG) to enable dynamic, context-aware intervention. The approach requires no architectural modification to the base LLM and integrates transparently into existing inference pipelines. Experiments demonstrate that our method substantially reduces copyrighted content reproduction rates while preserving generation quality, achieving high prediction accuracy, low computational overhead, and strong scalability across diverse model sizes and domains. The implementation is publicly available.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Large Vision Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs' internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. The implementation is available on GitHub.footnote{https://github.com/changhu73/Internal_states_leakage}

Problem

• Detect copyrighted data leakage risks in LLMs before text generation
• Proactively prevent exposure of sensitive information using internal state analysis
• Ensure copyright compliance while maintaining high-quality AI text output

Innovation

• Analyzes internal states pre-generation for leakage detection
• Uses neural network classifier for early risk identification
• Integrates with RAG system for copyright compliance