📝 SummarXiv

Abstract

Phishing is a pervasive form of social engineering in which attackers impersonate trusted entities to steal information or induce harmful actions. Text-based phishing dominates for its low cost, scalability, and concealability, advantages recently amplified by large language models (LLMs) that enable ``Phishing-as-a-Service'' attacks at scale within minutes. Despite the growing research into LLM-facilitated phishing attacks, consolidated systematic research on the phishing attack life cycle remains scarce. In this work, we present the first systematization of knowledge (SoK) on LLM-generated phishing, offering an end-to-end analysis that spans generation techniques, attack features, and mitigation strategies. We introduce Generation-Characterization-Defense (GenCharDef), which systematizes the ways in which LLM-generated phishing differs from traditional phishing across methodologies, security perspectives, data dependencies, and evaluation practices. This framework highlights unique challenges of LLM-driven phishing, providing a coherent foundation for understanding the evolving threat landscape and guiding the design of more resilient defenses.