The rapid integration of Generative AI (GenAI) and Large Language Models (LLMs) in sectors such as education and healthcare have marked a significant advancement in technology. However, this growth has also led to a largely unexplored aspect: their security vulnerabilities. As the ecosystem that includes both offline and online models, various tools, browser plugins, and third-party applications continues to expand, it significantly widens the attack surface, thereby escalating the potential for security breaches. These expansions in the 6G and beyond landscape provide new avenues for adversaries to manipulate LLMs for malicious purposes. We focus on the security aspects of LLMs from the viewpoint of potential adversaries. We aim to dissect their objectives and methodologies, providing an in-depth analysis of known security weaknesses. This will include the development of a comprehensive threat taxonomy, categorizing various adversary behaviors. Also, our research will concentrate on how LLMs can be integrated into cybersecurity efforts by defense teams, also known as blue teams. We will explore the potential synergy between LLMs and blockchain technology, and how this combination could lead to the development of next-generation, fully autonomous security solutions. This approach aims to establish a unified cybersecurity strategy across the entire computing continuum, enhancing overall digital security infrastructure.

翻译：生成式人工智能（GenAI）与大语言模型（LLMs）在教育、医疗等领域的快速整合标志着技术的重大进步。然而，这一发展也暴露了一个尚未充分探索的方面：其安全漏洞。随着包含离线和在线模型、各类工具、浏览器插件及第三方应用在内的生态系统持续扩展，攻击面显著扩大，从而加剧了安全漏洞的潜在风险。在6G及未来通信场景下，这种扩展为攻击者操纵LLMs以实现恶意目的提供了新途径。本文从潜在攻击者的视角聚焦LLMs的安全问题，旨在剖析其目标与方法，对已知安全弱点进行深入分析，包括构建一套涵盖不同攻击行为的综合性威胁分类体系。同时，本研究将重点关注防御团队（即蓝队）如何将LLMs整合至网络安全工作中。我们将探索LLMs与区块链技术的潜在协同效应，以及这种结合如何推动下一代全自主安全解决方案的开发。该方法旨在建立覆盖整个计算连续体的统一网络安全策略，从而增强整体数字安全基础设施。