LLMs show promise in phishing detection but remain vulnerable

A new research paper explores the use of Large Language Models (LLMs) for detecting phishing emails, proposing a framework called LLMPEA. The study evaluates the effectiveness of frontier LLMs such as GPT-4o, Claude Sonnet 4, and Grok-3 in identifying various phishing attack vectors, including prompt injection and multilingual attacks. While LLMs demonstrated over 90% accuracy in detection, the research also highlights their susceptibility to adversarial exploitation, providing crucial insights for real-world LLM-based email security systems. AI

IMPACT LLMs can achieve high accuracy in phishing detection but require hardening against adversarial attacks.

RANK_REASON Research paper published on arXiv detailing a new framework for phishing detection using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Claude Sonnet 4
• GPT-4o
• Grok-3
• LLMPEA
• Shaohu Zhang

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →