Semantic communication (SemCom) improves communication efficiency by transmitting task-relevant information instead of raw bits and is expected to be a key technology for 6G networks. Recent advances in generative AI (GenAI) further enhance SemCom by enabling robust semantic encoding and decoding under limited channel conditions. However, these efficiency gains also introduce new security and privacy vulnerabilities. Due to the broadcast nature of wireless channels, eavesdroppers can also use powerful GenAI-based semantic decoders to recover private information from intercepted signals. Moreover, rapid advances in agentic AI enable eavesdroppers to perform long-term and adaptive inference through the integration of memory, external knowledge, and reasoning capabilities. This allows eavesdroppers to further infer user private behavior and intent beyond the transmitted content. Motivated by these emerging challenges, this paper comprehensively rethinks the security and privacy of SemCom systems in the age of generative and agentic AI. We first present a systematic taxonomy of eavesdropping threat models in SemCom systems. Then, we provide insights into how GenAI and agentic AI can enhance eavesdropping threats. Meanwhile, we also highlight potential opportunities for leveraging GenAI and agentic AI to design privacy-preserving SemCom systems.

翻译：语义通信通过传输任务相关信息而非原始比特流来提高通信效率，有望成为6G网络的关键技术。生成式人工智能的最新进展进一步增强了语义通信，使其能够在有限信道条件下实现鲁棒的语义编码与解码。然而，这些效率提升也引入了新的安全与隐私漏洞。由于无线信道的广播特性，窃听者同样可以利用基于生成式人工智能的强大语义解码器从截获的信号中恢复私密信息。此外，代理式人工智能的快速发展使得窃听者能够通过整合记忆、外部知识与推理能力，进行长期且自适应的推断。这使得窃听者能够超越传输内容本身，进一步推断用户的私密行为与意图。受这些新兴挑战的驱动，本文全面重新审视了生成式与代理式人工智能时代下语义通信系统的安全与隐私问题。我们首先提出了语义通信系统中窃听威胁模型的系统分类。随后，深入分析了生成式人工智能与代理式人工智能如何增强窃听威胁。同时，我们也强调了利用生成式人工智能与代理式人工智能设计隐私保护型语义通信系统的潜在机遇。