Authors: Yizhu Wang, Haoyu Zhai, Chenkai Wang, Qingying Hao, Nick A Cohen, Roopa Foulger, Jonathan A Handler, and Gang Wang.
Abstract
SMS phishing poses a significant threat to users, especially older adults. Existing defenses mainly focus on phishing detection, but often cannot explain why the SMS is malicious to lay users. In this paper, we use large language models (LLMs) to detect SMS phishing while generating evidence-based explanations. The key challenge is that SMS is short, lacking the necessary context for security reasoning. We develop a prototype called SmishX which gathers external contexts (e.g., domain and brand information, URL redirection, and web screenshots) to augment the chain-of-thought (CoT) reasoning of LLMs. Then, the reasoning process is converted into a short explanation message to help users with their decision-making. Evaluation using real-world SMS datasets shows SmishX can achieve an overall accuracy of 98.8%, outperforming existing methods. Through user studies (N=175), we show that SmishX’s explanation can significantly improve users’ phishing detection efficacy across age groups. Its usability is rated “excellent” by participants (SUS score 82.6). We conclude by discussing open challenges in resolving human-AI disagreements and safely handling AI errors.