Authors - Francklin Rivas, Thanh Tran, Jorge J Roman, Aysha Al Ketbi Abstract - The rapid proliferation of GenAI has transformed the phishing threat landscape into one characterized by realistic, tailored, and scalable attacks on text-based, web-based, and multimodal platforms. The success rate of social engineering attacks has increased significantly due to advances in large language models, deep-fake technology, and automated phishing-as-a-service offerings. Despite notable advances in current phishing detection technologies, many oper ate as black-box systems and struggle to detect AI-generated, context-specific, zero-day phishing attempts. The resulting lack of transparency, combined with poor realistic dataset quality and inadequate resilience against adaptive threats, has further amplified trust concerns. This survey presents a comprehensive over view of the detection strategies based on semantic, structural, and multi-quality feature representations, with a concise review of the models of GenAI-enabled phishing attacks. Various detection methodologies, including machine learning, deep learning, and fusion-based techniques, are reviewed, with an emphasis on explainable AI methods like SHAP, LIME, attention visualization, and Grad CAM, which provide more understandable interpretations of AI-driven deci sions. To facilitate transparent, reliable, and trustworthy phishing defenses that make use of GenAI, the survey concludes with discussions of response mecha nisms, privacy-preserving learning strategies, and governance issues, with open questions and potential directions for future research.
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