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MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs

Created by
  • Haebom

Author

Prakhar Sharma, Haohuang Wen, Vinod Yegneswaran, Ashish Gehani, Phillip Porras, Zhiqiang Lin

MobiLLM: An Autonomous Security Framework for 6G O-RAN Environments

Outline

This paper presents MobiLLM, an agent-based AI framework for fully automated end-to-end threat mitigation in 6G open-access radio (O-RAN) environments. MobiLLM orchestrates security workflows through a modular multi-agent system based on Large Language Models (LLMs). The framework features a threat analysis agent for real-time data classification, a threat classification agent that maps anomalies to specific countermeasures using Retrieval-Augmented Generation (RAG), and a threat response agent that securely operates mitigation actions via the O-RAN control interface. Built on a trusted knowledge base, such as the MITRE FiGHT framework and 3GPP specifications, and equipped with robust safety guardrails, MobiLLM provides a blueprint for reliable AI-based network security. Initial evaluations demonstrate that MobiLLM effectively identifies and orchestrates complex mitigation strategies, significantly reducing response latency and demonstrating the feasibility of autonomous security operations in 6G.

Takeaways, Limitations

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Presenting a new framework for automated threat mitigation in 6G O-RAN environments.
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Automating security workflows using an LLM-based multi-agent system.
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Improving threat classification accuracy by leveraging RAG technology.
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Leverage trusted knowledge bases such as the MITRE FiGHT framework and 3GPP specifications.
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Demonstrated reduced response latency and autonomous security operation capabilities.
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Current O-RAN applications are primarily focused on network optimization or passive threat detection, which needs to be improved.
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Scalability and performance verification in large-scale network environments is required.
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Further research is needed into the potential for malfunctions or biases in AI-based systems.
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Further research is needed on the effectiveness of safety guardrails and how to prevent potential misuse.
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