  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.
