Generative AI (GAI) models have been rapidly advancing, with a wide range of applications including intelligent networks and mobile AI-generated content (AIGC) services. Despite their numerous applications and potential, such models create opportunities for novel security challenges. In this paper, we examine the challenges and opportunities of GAI in the realm of the security of intelligent network AIGC services such as suggesting security policies, acting as both a ``spear'' for potential attacks and a ``shield'' as an integral part of various defense mechanisms. First, we present a comprehensive overview of the GAI landscape, highlighting its applications and the techniques underpinning these advancements, especially large language and diffusion models. Then, we investigate the dynamic interplay between GAI's spear and shield roles, highlighting two primary categories of potential GAI-related attacks and their respective defense strategies within wireless networks. A case study illustrates the impact of GAI defense strategies on energy consumption in an image request scenario under data poisoning attack. Our results show that by employing an AI-optimized diffusion defense mechanism, energy can be reduced by 8.7%, and retransmission count can be decreased from 32 images, without defense, to just 6 images, showcasing the effectiveness of GAI in enhancing network security.

翻译：生成式人工智能（GAI）模型近年来迅速发展，其应用范围涵盖智能网络和移动端人工智能生成内容（AIGC）服务。尽管这些模型具有众多应用场景和巨大潜力，但它们也带来了新型安全挑战。本文探讨了GAI在智能网络AIGC服务安全领域（例如安全策略建议）中的挑战与机遇——它既能作为潜在攻击的“矛”，也能作为各类防御机制中不可或缺的“盾”。首先，我们全面概述了GAI的发展现状，重点介绍其应用场景及支撑技术（尤其是大型语言模型和扩散模型）的进展。随后，我们研究了GAI“矛”与“盾”角色的动态博弈，系统归纳了无线网络中两类潜在的GAI相关攻击及其对应防御策略。通过案例分析，我们展示了在数据投毒攻击场景下，GAI防御策略对图像请求任务中能耗的影响。结果表明，采用AI优化的扩散防御机制后，能耗降低了8.7%，重传图像数量从无防御时的32张减少至仅6张，充分验证了GAI在增强网络安全方面的有效性。