Targeted Phishing Campaigns using Large Scale Language Models

TL;DR

This paper investigates how large-scale language models like GPT-3 can generate convincing phishing emails, highlighting their potential to increase attack success rates and the importance of addressing associated security risks.

Contribution

The study presents a framework for evaluating NLMs in phishing email generation and demonstrates their ability to produce effective, hard-to-detect messages.

Findings

NLMs can generate phishing emails that bypass spam filters.

Generated emails have a high success rate in tricking individuals.

Effectiveness varies with different models and training data.

Abstract

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.