When Eavesdroppers Reason: Agentic Eavesdropping Attacks on Semantic Communication
Shunpu Tang, Qianqian Yang, Zhiguo Shi, Jiming Chen, Xuemin Shen

TL;DR
This paper introduces an agentic eavesdropper powered by large language models that can effectively breach privacy in semantic communication systems without needing wiretap channel information, revealing significant security risks.
Contribution
It proposes a novel LLM-based agentic eavesdropper with a closed-loop workflow that outperforms existing methods in privacy inference without wiretap CSI.
Findings
Achieves over 75% eavesdropping success rate at SNR ≥ 5 dB
Operates effectively without wiretap channel state information
Highlights severe privacy risks in current SemCom architectures
Abstract
Semantic communication (SemCom) has emerged as a promising paradigm for next-generation networks. However, its typical end-to-end joint source--channel coding (JSCC) architecture also raises serious privacy concerns. To guide future secure SemCom design, it is important to understand how serious such leakage can be. Nevertheless, existing eavesdropping attacks mainly rely on fixed-configuration solvers and often require instantaneous wiretap channel state information (CSI) to achieve effective privacy inference. This may lead future secure SemCom designs to overlook potentially severe risks. To address this, we propose a large language model (LLM)-orchestrated agentic eavesdropper. Specifically, the proposed eavesdropper forms a closed-loop workflow with three functional agents. The optimization agent adaptively performs joint semantic-and-channel inversion to recover private…
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