Rethinking Secure Semantic Communications in the Age of Generative and Agentic AI: Threats and Opportunities

2026-01-05

AI Summary

This work addresses emerging security threats in semantic communication systems introduced by generative and agent-based artificial intelligence. While these AI paradigms enhance semantic communication efficiency, they also enable novel attack vectors—such as leveraging generative models to decode intercepted signals or deploying embodied agents capable of long-term, adaptive inference to infer user intent. The paper presents the first threat classification framework specifically tailored to generative and agent AI in semantic communication, systematically uncovering their unique risks of prolonged inference-based eavesdropping. It further explores the dual role of AI as both a threat and a defense mechanism. By integrating generative AI, memory-augmented reasoning agents, and semantic communication modeling, this study elucidates new eavesdropping mechanisms and establishes a theoretical foundation and technical pathway for designing privacy-enhanced semantic communication systems resilient to AI-powered eavesdropping.

Technology Category

• Multiagent Systems: Agent Communication
• Philosophy and Ethics of AI: Privacy & Security
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Semantic Communication
• Generative AI
• Agentic AI
• Security
• Privacy

Innovation

• Semantic Communication
• Generative AI
• Agentic AI
• Privacy-Preserving Design
• Eavesdropping Threat Model