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.

翻译：钓鱼作为一种数十年来普遍存在的网络犯罪手段，至今仍是数字世界中的重大威胁。通过巧妙运用社会工程学元素和现代技术，网络犯罪分子将大量个人、企业和组织作为目标，利用信任与安全漏洞实施攻击。这些网络攻击者常以可信形式伪装，冒充合法来源。通过巧妙运用紧迫感、恐惧、社会认同等心理操纵策略及各类操控手段，钓鱼者能诱使个人泄露敏感隐私信息。基于这一现代技术中的普遍性问题，本文旨在分析15种大型语言模型（LLMs）在检测钓鱼尝试中的有效性，特别聚焦于一组随机选取的"419诈骗"邮件。研究目标是通过分析包含邮件元数据的文本文件，依据预设标准判定哪些LLM能准确识别钓鱼邮件。实验结果表明，ChatGPT 3.5、GPT-3.5-Turbo-Instruct与ChatGPT在检测钓鱼邮件方面表现最为有效。