In this research, we aim to explore the potential of natural language models (NLMs) such as GPT-3 and GPT-2 to generate effective phishing emails. Phishing emails are fraudulent messages that aim to trick individuals into revealing sensitive information or taking actions that benefit the attackers. We propose a framework for evaluating the performance of NLMs in generating these types of emails based on various criteria, including the quality of the generated text, the ability to bypass spam filters, and the success rate of tricking individuals. Our evaluations show that NLMs are capable of generating phishing emails that are difficult to detect and that have a high success rate in tricking individuals, but their effectiveness varies based on the specific NLM and training data used. Our research indicates that NLMs could have a significant impact on the prevalence of phishing attacks and emphasizes the need for further study on the ethical and security implications of using NLMs for malicious purposes.
翻译:在这一研究中,我们的目标是探索诸如GPT-3和GPT-2等自然语言模型(NLMs)在生成有效的钓鱼电子邮件方面的潜力。钓鱼邮件是欺骗性信息,目的是诱使个人披露敏感信息或采取有利于攻击者的行动。我们提出了一个框架,用于评估NLMs在根据各种标准生成这类电子邮件方面的表现,包括生成文本的质量、绕过垃圾过滤器的能力以及欺骗者的成功率。我们的评估表明,NLMs能够生成难以检测和在欺骗个人方面成功率很高的钓鱼电子邮件,但其效力根据具体的NLMM和所使用的培训数据而有所不同。我们的研究显示,NLMs可能会对钓鱼攻击的流行产生重大影响,并强调需要进一步研究恶意使用NLMs的道德和安全影响。