EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass filters. Traditional spam detection systems, which rely on static rules or single-modality models, struggle to integrate heterogeneous signals or to continuously adapt, leading to rapid performance degradation.We propose EvoMail, a self-evolving cognitive agent framework for robust detection of spam and phishing. EvoMail first constructs a unified heterogeneous email graph that fuses textual content, metadata (headers, senders, domains), and embedded resources (URLs, attachments). A Cognitive Graph Neural Network ( Model 1. ) enhanced by a Large Language Model (LLM) performs context-aware reasoning across these sources to identify coordinated spam campaigns. Most critically, EvoMail engages in an adversarial self-evolution loop: a “red-team” agent generates novel evasion tactics—such as character obfuscation or AI-generated phishing text—while the “blue-team” detector learns from failures, compresses experiences into a memory module, and reuses them for future reasoning.Extensive experiments on real-world datasets (Enron-Spam, Ling-Spam, SpamAssassin, and TREC) and synthetic adversarial variants demonstrate that EvoMail consistently outperforms state-of-the-art baselines in detection accuracy, adaptability to evolving spam tactics, and interpretability of reasoning traces. These results highlight EvoMail’s potential as a resilient and explainable defense framework against next-generation spam and phishing threats. Supplementary Material File (eswileyevomail.pdf) - Download - 1.09 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 167views 108downloads Citations Download citation Wei Huang, Haifeng Ling, Linyuan Bai, et al. EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense. Authorea. 15 October 2025. DOI: https://doi.org/10.22541/au.176050645.55413642/v1 DOI: https://doi.org/10.22541/au.176050645.55413642/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.