AIにより推定されたラベル
LLMセキュリティ インダイレクトプロンプトインジェクション プライバシー管理
※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract
Protecting cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities presents a double-edged sword that 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, significantly reducing the incentives for IP creators to contribute, and leading 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 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 outperforming traditional defenses such as configuration-based restrictions.