Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Large language models in 6G security: challenges and opportunities

Tri Nguyen, Huong Nguyen, Ahmad Ijaz, Saeid Sheikhi, Athanasios V. Vasilakos, Panos Kostakos

TL;DR

The paper investigates security challenges for Generative AI and LLMs in the 6G and beyond landscape, distinguishing AI-inherent and non-AI vulnerabilities and proposing a threat taxonomy. It outlines blue-team defenses through training-safety strategies and LLMSecOps, mapping capabilities to the NIST Identify/Protect/Detect/Respond framework and highlighting practical tools and platforms. The work discusses LLMSecOps in 6G, including IBN, NWDAF, and Zero-Touch security, and envisions autonomous LLM agent swarms supported by blockchain and TEEs to enable secure distributed cooperation. Finally, it presents open research questions across three axes—training safety, SecOps integration, and secure autonomous swarms—aiming to guide future work toward resilient, autonomous, edge-cloud secure 6G infrastructures.

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.

Large language models in 6G security: challenges and opportunities

TL;DR

The paper investigates security challenges for Generative AI and LLMs in the 6G and beyond landscape, distinguishing AI-inherent and non-AI vulnerabilities and proposing a threat taxonomy. It outlines blue-team defenses through training-safety strategies and LLMSecOps, mapping capabilities to the NIST Identify/Protect/Detect/Respond framework and highlighting practical tools and platforms. The work discusses LLMSecOps in 6G, including IBN, NWDAF, and Zero-Touch security, and envisions autonomous LLM agent swarms supported by blockchain and TEEs to enable secure distributed cooperation. Finally, it presents open research questions across three axes—training safety, SecOps integration, and secure autonomous swarms—aiming to guide future work toward resilient, autonomous, edge-cloud secure 6G infrastructures.

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.
Paper Structure (16 sections, 2 figures)

This paper contains 16 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Attacks - Red teaming
  3. AI-related vulnerabilities
  4. Non-AI-related vulnerability
  5. Defense - Blue teaming
  6. Strategies for LLM training safety
  7. Taxonomy and LLMSecOps applications
  8. LLMSecOps in the 6G era
  9. Intent-based networking
  10. Network Data Analytics Function
  11. Zero-Touch Network 5G/6G Security
  12. Autonomous LLM Agent Swarms
  13. The transition to distributed LLMs
  14. Security and trust in distributed LLMs
  15. Autonomous defense framework
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Process and components of IBN.
  • Figure 2: Autonomous defense with LLM agent swarms.