[论文解读] Large language models in 6G security: challenges and opportunities

Tri Q. Nguyen, Huong Lan Thi Nguyen+4 more|arXiv (Cornell University)|Mar 18, 2024

DNA and Biological Computing被引用 8

一句话总结

本论文分析6G时代大语言模型（LLMs）的安全漏洞，提出威胁分类法，并讨论防御策略、LLMSecOps，以及包括与区块链集成的自治安全解决方案。

ABSTRACT

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.

研究动机与目标

• 识别6G生态系统中LLMs的安全漏洞及对手目标。
• 为GenAI和LLM安全制定全面的威胁分类体系。
• 探索在网络安全运营中使用LLMs的防御策略与蓝队集成。
• 提出LLMSecOps概念及面向6G的自治、集成安全架构。
• 讨论LLMs与区块链在下一代安全中的潜在协同作用。

提出的方法

• 调查已知的LLM安全弱点，并将对抗性行为分为AI固有与非AI固有漏洞。
• 给出GenAI/LLMs的威胁分类法，并结合OWASP及相关研究的示例。
• 评审在预处理、检测和后处理阶段的LLM训练安全与安全推理的防御策略。
• 讨论与NIST和OWASP框架对齐的LLMSecOps概念，以在6G边云连续体中实现网络防御。
• 描述用于与LLMs和AI驱动安全集成的自治、安全的6G网络的体系结构与组件（IBN、NWDAF、ZSM）。
• 突出案例研究与工具（如PentestGPT、PAC-GPT、基于GPT的网络防御系统）以说明实际的LLMSecOps部署

Figure 1: Process and components of IBN.

实验结果

研究问题

• RQ1 在6G环境中影响LLMs的主要AI相关与非AI相关漏洞有哪些？
• RQ2 如何构建GenAI/LLMs的威胁分类法以指导安全部署与防御？
• RQ3 哪些防御策略和蓝队实践可以降低现实世界6G环境中的LLM安全风险？
• RQ4 LLMSecOps及相关架构（IBN、NWDAF、ZSM）如何实现自治、可信的6G网络？
• RQ5 LLMs结合区块链在提升下一代安全解决方案中可能的作用是什么？

主要发现

• 初步的漏洞分类识别出提示注入、输出处理、数据中毒、模型盗用、拒绝服务、数据披露、不安全的插件以及后门/零日风险作为关键的AI相关弱点。
• 非AI漏洞包括远程代码执行、侧信道风险以及影响LLM生态系统的不安全插件。
• LLMs可以通过训练安全、提示处理、恶意输入检测和后处理验证步骤来支持蓝队行动。
• 提出了若干体系结构与框架（IBN、NWDAF、ZSM），以实现与LLMs集成的自治、安全的6G网络。
• 实证示例与原型（PentestGPT、PAC-GPT、LogBERT、Cyber Sentinel、HuntGPT）说明了实际的LLMSecOps应用与防御增强。
• 论文主张在6G计算连续性上制定统一、符合治理的网络安全策略，并设想通过LLMSecOps与区块链集成实现自治安全。

Figure 2: Autonomous defense with LLM agent swarms.