Prompt Engineering for Detecting Phishing

This paper introduces an adversarial framework that leverages two Large Language Models (LLMs) via prompt engineering to enhance phishing detection. One LLM functions as a generator, producing sophisticated phishing emails that mimic legitimate communications, while the other serves as a discriminator, detecting and classifying these emails and providing detailed reasoning for its decisions. By dynamically refining prompts based on adversarial interactions, this framework not only improves detection accuracy but also educates users on phishing indicators-helping reduce cognitive biases. Our results demonstrate a robust, adaptive defense against increasingly complex cyber threats.