ISACL : Internal State Analyzer for Copyrighted Training Data Leakage (2025)

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. 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. Our code can be found here: (https://anonymous.4open.science/r/Internal-states-leakage-9D6E).

## introduction llms have significantly enhanced text generation and dialogue systems in nlp @xcite . however, they also pose risks of unintentionally reproducing copyrighted or proprietary information from their training data, especially when the data is licensed for training but not distribution. according to u.s. copyright law (u.s. copyright @xcite , only the copyright holder has the exclusive right to distribute copyrighted works. if an llm inadvertently distributes copyrighted material by replicating parts of its training data, it may violate this law and expose its developers or users to legal liability @xcite . this underscores the importance of adhering to legal and ethical standards when deploying llms across various applications @xcite @xcite @xcite . addressing these risks is crucial to protecting intellectual property rights and ensuring the responsible and lawful use of llms in real-world situations. previous research has raised concerns about the issue of copyrighted data leakage @xcite @xcite during the generation process of llms, including the leakage of private information @xcite @xcite and evaluation data used in machine learning @xcite . existing methods to prevent or mitigate data leakage include implementing strict output filtering @xcite and context-aware mechanisms @xcite , applying differential privacy techniques @xcite @xcite @xcite @xcite @xcite or other data anonymization methods during training, regularly auditing and reviewing model outputs, and monitoring llm interactions to detect potential data leakage. however, these approaches face several limitations, such as limited coverage of scenarios @xcite , reduced model performance and usability caused by differential privacy techniques, and high costs and delays associated with manual audits @xcite . to address the challenge of detecting copyrighted training data leakage in llm-generated text, we introduce a framework called the internal state analyzer for copyrighted training data leakage (isacl). isacl evaluates leakage risks by analyzing the model's internal states during the prefill phase, before any text is generated. unlike conventional approaches that rely on examining fully generated outputs, isacl proactively identifies potential risks by assessing early-stage representations of input text and their correlation with copyrighted reference materials. this approach enables real-time, scalable, and precise risk assessment without requiring complete output generation. to enhance its effectiveness, isacl is integrated into a rag system. copyrighted information is indexed using faiss and stored in sqlite, allowing efficient retrieval of relevant reference materials during the evaluation process. when a relevant reference is retrieved, it is combined with the model's internal states to determine the likelihood of leakage. this integration improves the accuracy and efficiency of comparing generated content with known copyrighted training data, ensuring reliable detection. beyond detection, isacl adheres to legal and ethical standards, serving as a robust safeguard against unauthorized disclosure of copyrighted materials in ai-generated content. by ensuring compliance with licensing constraints, isacl promotes responsible and lawful use of llms in real-world applications. in a series of experimental configurations, isacl demonstrated outstanding performance, achieving high accuracy and f1 scores. specifically, accuracy ranged from 91.88% to 95.05%, while f1 scores varied between 0.9249 and 0.9468. in certain configurations, isacl even achieved near-perfect detection rates. these results highlight isacl's consistent ability to accurately identify potential training-set leakage across diverse settings, maintaining high levels of precision and recall. the findings underscore isacl's robustness in scalable, real-time risk detection for llm-generated content, even without generating any text. for a detailed description of the experimental setup and results, please refer to section 4.3. our primary contributions are as follows: • as illustrated in figure 1 , we propose a real-time framework "isacl" for predicting copyrighted training data leakage in llm-generated text by leveraging internal states extracted before any token is decoded. this ensures efficiency and avoids reliance on output generation, proactively addressing potential risks of unauthorized disclosure. ## experiments in this section, we evaluate the effectiveness of the internal states judge in identifying literal copying leakage (according to strict text similarity) in text continuations. specifically, we address the following research questions (rq): ## conclusion and future work this study introduces isacl, a framework designed to detect copyrighted training data leakage in llm-generated text by analyzing internal states during the prefill phase, before any text is generated. unlike traditional methods that analyze fully generated outputs, isacl enables proactive, real-time detection by examining early-stage representations of input text in relation to copyrighted reference materials. experiments with models like llama and mistral show that larger models achieve higher accuracy due to richer internal representations. to enhance its effectiveness, isacl is integrated into a rag system, using faiss for vector search and sqlite for structured storage. this integration allows efficient retrieval of relevant copyrighted materials and combines them with the model's internal states to assess leakage risks, ensuring compliance with licensing constraints while improving detection accuracy and efficiency. future work will focus on addressing more complex forms of copyright leakage, such as conceptual similarity and paraphrasing, and refining the framework for better robustness and interpretability. additionally, we aim to develop an llm agent that actively prevents leakage by cross-referencing generated content against licensed or publicly available materials, ensuring real-time compliance with data usage policies.