With the growing development and deployment of large language models (LLMs) in both industrial and academic fields, their security and safety concerns have become increasingly critical. However, recent studies indicate that LLMs face numerous vulnerabilities, including data poisoning, prompt injections, and unauthorized data exposure, which conventional methods have struggled to address fully. In parallel, blockchain technology, known for its data immutability and decentralized structure, offers a promising foundation for safeguarding LLMs. In this survey, we aim to comprehensively assess how to leverage blockchain technology to enhance LLMs' security and safety. Besides, we propose a new taxonomy of blockchain for large language models (BC4LLMs) to systematically categorize related works in this emerging field. Our analysis includes novel frameworks and definitions to delineate security and safety in the context of BC4LLMs, highlighting potential research directions and challenges at this intersection. Through this study, we aim to stimulate targeted advancements in blockchain-integrated LLM security.

翻译：随着大语言模型在工业界和学术界的日益发展与部署，其安全与防护问题变得愈发关键。然而，近期研究表明，大语言模型面临诸多漏洞，包括数据投毒、提示注入和未经授权的数据暴露等，而传统方法难以全面应对。与此同时，以其数据不可篡改性和去中心化结构著称的区块链技术，为保障大语言模型安全提供了极具前景的基础。本综述旨在全面评估如何利用区块链技术来增强大语言模型的安全与防护。此外，我们提出了一个面向大语言模型的区块链技术新分类法，以系统化地归类这一新兴领域的相关工作。我们的分析包含用于界定BC4LLMs背景下安全与防护的新颖框架与定义，并着重指出了该交叉领域的潜在研究方向与挑战。通过本研究，我们旨在推动区块链与大语言模型安全融合领域的针对性进展。