@inproceedings{zhong-etal-2025-web,
    title = "Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models",
    author = "Zhong, Yisheng  and
      Wen, Yizhu  and
      Guo, Junfeng  and
      Kafai, Mehran  and
      Huang, Heng  and
      Guo, Hanqing  and
      Zhu, Zhuangdi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.870/",
    pages = "17222--17235",
    ISBN = "979-8-89176-332-6",
    abstract = "The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AI-generated content. In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. Our method follows principled motivations and effectively addresses an intractable black-box optimization problem. Real-world experiments demonstrated that our methods improve defense success rates from 2.5{\%} to 88.6{\%} on different LLMs, outperforming traditional defenses such as configuration-based restrictions."
}