[Paper Review] 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 Computing8 citations

TL;DR

This paper analyzes security vulnerabilities of large language models (LLMs) in the 6G era, proposes a threat taxonomy, and discusses defense strategies, LLMSecOps, and autonomous security solutions including integration with blockchain.

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.

Motivation & Objective

• Identify security vulnerabilities and adversary objectives for LLMs in 6G ecosystems.
• Develop a comprehensive threat taxonomy for GenAI and LLM security.
• Explore defense strategies and blue-team integrations using LLMs in cybersecurity operations.
• Propose LLMSecOps concepts and autonomous, integrated security architectures for 6G.
• Discuss the potential synergy between LLMs and blockchain for next-generation security.

Proposed method

• Survey known LLM security weaknesses and categorize adversarial behaviors into AI-inherent and non-AI-inherent vulnerabilities.
• Present a threat taxonomy for GenAI/LLMs with examples from OWASP and related studies.
• Review defense strategies for LLM training safety and secure inference across pre-processing, detection, and post-processing stages.
• Discuss LLMSecOps concepts aligned with NIST and OWASP frameworks to enable cyber defense in the 6G edge-cloud continuum.
• Describe architectures and components (IBN, NWDAF, ZSM) for autonomous, secure 6G networks integrated with LLMs and AI-enabled security.
• Highlight case studies and tools (e.g., PentestGPT, PAC-GPT, GPT-based cyber defense systems) to illustrate practical LLMSecOps deployment

Figure 1: Process and components of IBN.

Experimental results

Research questions

• RQ1 What are the primary AI-related and non-AI-related vulnerabilities affecting LLMs in 6G contexts?
• RQ2 How can a threat taxonomy for GenAI/LLMs be structured to guide secure deployment and defense?
• RQ3 What defense strategies and blue-team practices can mitigate LLM security risks in real-world 6G environments?
• RQ4 How can LLMSecOps and related architectures (IBN, NWDAF, ZSM) enable autonomous, secure 6G networks?
• RQ5 What is the potential role of LLMs in conjunction with blockchain to enhance next-generation security solutions?

Key findings

• A preliminary vulnerability taxonomy identifies prompt injection, output handling, data poisoning, model theft, DoS, data disclosure, insecure plugins, and backdoor/zero-day risks as key AI-related weaknesses.
• Non-AI vulnerabilities include remote code execution, side-channel risks, and insecure plugins impacting LLM ecosystems.
• LLMs can support blue-team operations through training safety, prompt processing, malicious input detection, and post-processing verification steps.
• Several architectures and frameworks (IBN, NWDAF, ZSM) are proposed to enable autonomous, secure 6G networks integrated with LLMs.
• Empirical examples and prototypes (PentestGPT, PAC-GPT, LogBERT, Cyber Sentinel, HuntGPT) illustrate practical LLMSecOps applications and defense enhancements.
• The paper argues for a unified, governance-aligned cybersecurity strategy across the 6G computing continuum and envisions autonomous security via LLMSecOps and blockchain integration.

Figure 2: Autonomous defense with LLM agent swarms.