SoK: Systematizing Generation, Characteristics, and Defenses in LLM-Generated Phishing
This systematization maps how LLMs enable phishing at unprecedented scale and catalogues which defences hold up, giving SOC teams a structured basis for upgrading detection pipelines.
Summary written by editorial AI · Source link below
arXiv:2508.21457v4 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs), with their growing abuse in phishing, has enabled phishing content, including deceptive pretexts and other persuasive elements, to be generated at a scale difficult to achieve manually. This growing misuse of LLMs in phishing raises questions about potential LLM-driven changes in phishing characteristics, associated security implications for users, and the resulting challenges to existing
Editorial Analysis
AI-generated phishing is eroding the effectiveness of traditional detection heuristics; enterprises need to understand which defensive layers remain reliable.
Benchmark your email-security stack against LLM-generated phishing samples to identify detection blind spots.
AI-powered phishing is outpacing legacy defences; updating detection and awareness programs is now a board-level priority.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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