Author Identifier (ORCID)
Abstract
The vision for 6G aims to enhance network capabilities, supporting an intelligent digital ecosystem where artificial intelligence (AI) is a key. However, the expansion of 6G raises critical security and privacy concerns due to the increased integration of IoT devices, edge computing, and AI. This survey provides a comprehensive overview of 6G protocols with a focus on security and privacy, identifying risks that have not been experienced in preceding 5G systems, and presenting mitigation strategies. While many vulnerabilities from earlier generations persist, the introduction of AI/ML introduces novel risks like model inversion and malicious manipulation of AI. Vulnerabilities in emerging personal IoT networks and autonomous vehicles are also underscored, where falsified command signaling or privacy leakage can pose safety and ethical concerns. The survey also discusses the transition toward lattice-based, post-quantum encryption standards, and identifies limitations in current security frameworks and calls for new, dynamic approaches tailored to 6G’s complexity. Close collaboration among stakeholders, including governments, industry, and researchers, is indispensable to developing robust standards, secure architectures, and risk assessment frameworks that address AI, quantum threats, and privacy at scale.
Keywords
6G, network protocols, security and privacy, wireless communication
Document Type
Journal Article
Date of Publication
6-1-2026
Volume
58
Issue
8
Publication Title
ACM Computing Surveys
Publisher
Association for Computing Machinery
School
School of Engineering
Funders
Australian Government’s Department of Home Affairs
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Yang, M., Qu, Y., Ranbaduge, T., Thapa, C., Sultan, N. H., Ding, M., Suzuki, H., Ni, W., Abuadbba, S., Smith, D., Tyler, P., Pieprzyk, J., Rakotoarivelo, T., Guan, X., & Mrabet, S. (2026). From 5G to 6G: A survey on security, privacy, and standardization pathways. ACM Computing Surveys, 58(8), 1–38. https://doi.org/10.1145/3785467