  Phishing, a prevalent cybercrime tactic for decades, remains a significant
threat in today's digital world. By leveraging clever social engineering
elements and modern technology, cybercrime targets many individuals,
businesses, and organizations to exploit trust and security. These
cyber-attackers are often disguised in many trustworthy forms to appear as
legitimate sources. By cleverly using psychological elements like urgency,
fear, social proof, and other manipulative strategies, phishers can lure
individuals into revealing sensitive and personalized information. Building on
this pervasive issue within modern technology, this paper aims to analyze the
effectiveness of 15 Large Language Models (LLMs) in detecting phishing
attempts, specifically focusing on a randomized set of "419 Scam" emails. The
objective is to determine which LLMs can accurately detect phishing emails by
analyzing a text file containing email metadata based on predefined criteria.
The experiment concluded that the following models, ChatGPT 3.5,
GPT-3.5-Turbo-Instruct, and ChatGPT, were the most effective in detecting
phishing emails.
