# Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs

arXiv: https://arxiv.org/abs/2411.08640 · HTML: https://mlsanity.com/paper/2411.08640/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models](https://mlsanity.com/paper/2407.15248/) (2024)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms](https://mlsanity.com/paper/2304.11125/) (2023)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Integrating LLMs for Explainable Fault Diagnosis in Complex Systems](https://mlsanity.com/paper/2402.06695/) (2024)
- [AI for Beyond 5G Networks: A Cyber-Security Defense or Offense Enabler?](https://mlsanity.com/paper/2201.02730/) (2022)
- [Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook](https://mlsanity.com/paper/2303.06223/) (2023)
- [Evaluating the Security of Open Radio Access Networks](https://mlsanity.com/paper/2201.06080/) (2022)
- [Joint Power Allocation and Network Slicing in an Open RAN System](https://mlsanity.com/paper/1911.01904/) (2019)
- [Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture](https://mlsanity.com/paper/2309.13444/) (2023)
- [Blockchain and Deep Learning-Based IDS for Securing SDN-Enabled Industrial IoT Environments](https://mlsanity.com/paper/2401.00468/) (2023)
- [Blockchain-enabled Network Sharing for O-RAN in 5G and Beyond](https://mlsanity.com/paper/2107.02005/) (2021)
- [Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning](https://mlsanity.com/paper/2401.08861/) (2024)

## Built on by (most cited first, 3 of 3)

- [ORANSight-2.0: Foundational LLMs for O-RAN](https://mlsanity.com/paper/2503.05200/) (2025)
- [Generative Resource Allocation for 6G O-RAN with Diffusion Policies](https://mlsanity.com/paper/2506.07880/) (2025)
- [Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study](https://mlsanity.com/paper/2605.14387/) (2026)

## Abstract (arXiv)

The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement, O-RAN is not without risks that need to be carefully assessed and properly addressed to accelerate its wide adoption in future mobile networks. In this paper, we present an in-depth security analysis of the O-RAN architecture, discussing the potential threats that may arise in the different O-RAN architecture layers and their impact on the Confidentiality, Integrity, and Availability (CIA) triad. We also promote the potential of zero trust, Moving Target Defense (MTD), blockchain, and large language models(LLM) technologies in fortifying O-RAN's security posture. Furthermore, we numerically demonstrate the effectiveness of MTD in empowering robust deep reinforcement learning methods for dynamic network slice admission control in the O-RAN architecture. Moreover, we examine the effect of explainable AI (XAI) based on LLMs in securing the system.