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Large language models in 6G security: challenges and opportunities

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arxiv 2403.12239 v1 pith:7LP6ULJQ submitted 2024-03-18 cs.CR cs.DC

classification cs.CRcs.DC
keywords securityllmsmodelspotentialwilladversariescybersecuritydevelopment
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Agentic AI in 6G Software Businesses: A Layered Maturity Model

    cs.SE 2025-08 conditional novelty 4.0 of 10

    A preliminary thematic review distills 29 motivators and 27 demotivators for agentic AI adoption in 6G software businesses into five themes each and outlines a future maturity model.

  2. Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management

    eess.SY 2025-06 conditional novelty 4.0 of 10

    A hierarchical debate framework, in which LLMs first decompose a 6G task and then refine each sub-task, improves keyword coverage over one-shot and regular single-level debate on the 6GPlan benchmark.

  3. Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networks

    cs.CR 2025-08 reject novelty 3.0 of 10

    Proposes FL+DP for offloading time-series transformer training in 6G vehicles, claiming 75% accuracy at ε=0.8, but lacks the described algorithms and has incorrect composition accounting.

  4. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

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