Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions

TL;DR

This paper reviews how generative AI techniques can enhance vulnerability detection in 6G wireless networks, addressing current challenges and proposing a multi-layer framework with practical case studies and future research directions.

Contribution

It introduces a comprehensive three-layer framework integrating various GAI models for 6G security and demonstrates a case study on LLM-driven code vulnerability detection.

Findings

LLM-driven code vulnerability detection is effective and promising.

The three-layer framework systematically analyzes GAI roles in 6G security.

Future directions include lightweight models and privacy-preserving techniques.

Abstract

The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability detection mechanisms. Traditional security methods, while foundational, struggle with zero-day exploits, adversarial threats, and context-dependent vulnerabilities in highly dynamic network environments. Generative AI (GAI) emerges as a transformative solution, leveraging synthetic data generation, multimodal reasoning, and adaptive learning to enhance security frameworks. This paper explores the integration of GAI-powered vulnerability detection in 6G wireless networks, focusing on code auditing, protocol security, cloud-edge defenses, and hardware protection. We introduce a three-layer framework comprising the Technology Layer, Capability Layer, and Application Layer to systematically analyze the role of VAEs, GANs, LLMs, and GDMs in securing next-generation wireless ecosystems. To demonstrate practical implementation, we present a case study on LLM-driven code vulnerability detection, highlighting its effectiveness, performance, and challenges. Finally, we outline future research directions, including lightweight models, high-authenticity data generation, external knowledge integration, and privacy-preserving technologies. By synthesizing current advancements and open challenges, this work provides a roadmap for researchers and practitioners to harness GAI for building resilient and adaptive security solutions in 6G networks.