The emergence of the Internet of Agents (IoA) introduces critical challenges for communication privacy in sensitive, high-stakes domains. While standard Agent-to-Agent (A2A) protocols secure message content, they are not designed to protect the act of communication itself, leaving agents vulnerable to surveillance and traffic analysis. We find that the rich, event-driven nature of agent dialogues provides a powerful, yet untapped, medium for covert communication. To harness this potential, we introduce and formalize the Covert Event Channel, the first unified model for agent covert communication driven by three interconnected dimensions, which consist of the Storage, Timing, and Behavioral channels. Based on this model, we design and engineer Pi-CCAP, a novel protocol that operationalizes this event-driven paradigm. Our comprehensive evaluation demonstrates that Pi-CCAP achieves high capacity and robustness while remaining imperceptible to powerful LLM-based wardens, establishing its practical viability. By systematically engineering this channel, our work provides the foundational understanding essential for developing the next generation of monitoring systems and defensive protocols for a secure and trustworthy IoA.

Kaibo Huang, Jin Tan, Yukun Wei, Wanling Li, Zipei Zhang, Hui Tian, Zhongliang Yang, Linna Zhou. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Steganalysis aims to detect the presence of concealed information within seemingly normal carriers in network transmissions, playing a crucial role in maintaining cybersecurity. With the rapid development of social networks, steganalysis techniques targeting social network texts have attracted significant interest from researchers in recent years. However, existing steganalysis techniques for social texts generally focus on analyzing the statistical features of the text itself, neglecting the relational features between texts, thereby limiting their detection capabilities. In this paper, we propose a novel text steganalysis feature enhancement method—TGCA. This method considers the relational features between texts by introducing GNN, while utilizing Transformers to expand the receptive field of GNNs and incorporating a cross-attention mechanism to reduce the aggregation of noise, thus mitigating the inherent limitations of GNNs. As a result, TGCA more effectively extracts and integrates textual and topological features, enhancing the model's performance in detecting steganographic texts. Experimental results demonstrate that TGCA outperforms existing methods by better leveraging graph and textual features to identify malicious steganographic texts. Our code is available at https://github.com/PandaaKai/TGCA.

With the rapid advancements in gene editing and DNA synthesis technologies, DNA has emerged as a next-generation physical steganographic medium due to its high information capacity, robustness, and superior concealment capabilities. While this steganography can be used to protect information security, it also poses a risk of being exploited for illicit transmission of harmful information. Consequently, it is imperative to discern steganographic DNA sequences from a vast array of DNA sequences. To address this challenge, this letter introduces a high-performance DNA steganalysis framework named DS-CLF. Specifically, the DS-CLF framework leverages a Transformer encoder to extract and learn DNA features within a supervised contrastive learning framework using ingeniously constructed DNA sequence triplets. Extensive experimental results demonstrate that the DS-CLF framework is highly effective in capturing DNA sequence features, and its detection capabilities for the latest DNA steganography techniques significantly outperform the best methods to date.