Can LLMs Keep Up? Evaluating Phishing Detection on Telegram

Social media platforms have become valuable sources for detecting phishing due to their widespread use and rapid content sharing. Among them, Telegram stands out as an open, data-rich environment, offering access to large public groups where phishing links frequently appear. This study investigates whether Large Language Models (LLMs) can serve as effective tools for phishing detection on Telegram. We systematically evaluate three representative LLMs — GPT-4o, LLaMA-3.1, and DeepSeek V3 — across multiple prompting strategies and input configurations. For comparison, we employ an improved version of traditional feature-based machine learning models from the literature, enhanced with resampling techniques to address the strong class imbalance in our data. Experimental results show that while LLMs demonstrate some potential, achieving a Recall for the phishing class of up to 0.554 with DeepSeek — representing the model’s ability to correctly identify 55.4% of all actual phishing messages — they suffer from extremely low precision (0.065 with DeepSeek), resulting in a low F1-score of 0.114. In contrast, our feature-based models achieve a significantly more reliable precision-recall balance, with an overall phishing F1-score of 0.749 and a precision of 0.738. The findings in this study highlight the potential and current limitations of LLMs for phishing detection in dynamic instant messaging environments and emphasize the superior performance of platform-tailored models.