Guardrailing LLM and agentic decisions for 6G AI-RAN - Tang, Yun; Zou, Mengbang; Guo, Weisi; Zaidi, Syed A. R. (Institute of Electrical and Electronics Engineers (IEEE), 2026)
Large language model (LLM)-based agents are envisioned as cornerstones for autonomous, zero-touch 6G AI-RAN operations. Numerous frameworks adopt LLM-based agents as decision-makers to optimize network configurations, orchestrate resources, and interact with users and connected use cases. However, intrinsic limitations (hallucinations, misaligned human values) and extrinsic adversarial threats (jail-breaks, prompt injections) pose critical risks to network safety, reliability, and privacy—challenges largely overlooked in existing literature. This paper addresses this gap by reviewing state-of-the-art guardrail techniques for 6G AI-RAN. We categorize guardrails across model-level and agent-level layers and map them to common agent application patterns in 6G networks, providing practical foundations for designing trustworthy agentic decision-making frameworks in future 6G AI-RAN systems.
Keywords: 46 Information and Computing Sciences; 4608 Human-Centred Computing; Generic health relevance; 6G; AI-RAN; LLM; LLM-based Agents; Guardrail