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

AI can automate security responses in next-generation networks: MobiLLM demonstrates how AI agents powered by Large Language Models can move beyond simply detecting threats to automatically responding to them in real-time, reducing the need for human security experts to manually intervene in every incident.

Open network architectures require new security approaches: The shift toward more open and flexible 6G networks (O-RAN) creates greater security risks, but the paper shows that AI-driven automation can help manage these risks at scale without proportionally increasing security costs or labor requirements.

Responsible AI requires built-in safety mechanisms: The framework incorporates "robust safety guardrails" and grounds decisions in trusted, established standards (MITRE FiGHT framework and 3GPP specifications) rather than relying solely on AI judgment, demonstrating that automated security systems need human-defined boundaries and knowledge bases.

Multi-agent AI systems can handle complex security workflows: By dividing security tasks among specialized agents (threat analysis, classification, and response), the framework shows how AI can break down complex problems into manageable pieces, making automated decision-making more transparent and controllable than a single "black box" system.

Trustworthiness requires grounding in domain expertise: The system's reliance on established telecom standards and security frameworks suggests that responsible AI in critical infrastructure should be built on existing expert knowledge rather than purely data-driven approaches, helping ensure decisions align with industry best practices.