Utilizing Large Language Models with Human Feedback Integration for Generating Dedicated Warning for Phishing Emails

With the rise of digital communication, phishing has emerged as the predominant cybercrime. Automated detection systems encounter challenges such as user trust issues and false positives, while human-centric solutions are resource-intensive and struggle with sophisticated attacks. Despite this threat, research on empowering users with automatic anti-phishing systems remains limited. This paper introduces a human-centric framework that utilizing Large Language Models (LLMs) to extract phishing indicators and generate meaningful warnings.