Phishing attacks are a constant threat, and malicious URLs are often the entry point for devastating security breaches. Traditional methods for detecting these URLs, like blacklisting, struggle to keep up with the ever-evolving tactics of attackers. Machine learning has stepped in to fill the gap, but existing models often falter when faced with URLs from sources outside their training data. What if there was a way to detect phishing URLs with minimal training data? New research explores the surprising ability of Large Language Models (LLMs) to act as one-shot URL classifiers. The study found that by giving an LLM just one example of a benign URL and prompting it to reason through the characteristics of a new URL, these models can achieve accuracy comparable to traditional methods trained on massive datasets. This approach leverages the inherent knowledge LLMs possess about the structure and characteristics of websites gleaned from their vast training data. Imagine an LLM dissecting a URL, recognizing whether the domain, subdomain, and path align with known legitimate entities or exhibit telltale signs of phishing. The research tested several leading LLMs, including GPT-4 Turbo, Claude 3 Opus, Gemini, and LLaMA models, on three different URL datasets. GPT-4 Turbo emerged as the top performer, demonstrating an impressive ability to identify malicious URLs with limited guidance. Interestingly, increasing the number of examples provided to the LLMs didn't significantly improve accuracy, highlighting the power of their existing knowledge base. The LLMs weren’t just classifying; they were also explaining their reasoning. The research delved into the quality of these explanations, finding that they often aligned with the insights of traditional methods. This explainability is a crucial element, potentially empowering everyday users to understand the risks associated with URLs and make more informed decisions. While this research focuses solely on URL features, future work could explore incorporating multimodal information, like webpage visuals and code, to create even more robust phishing detectors. This one-shot approach could revolutionize how we defend against phishing attacks, offering a rapid and effective way to identify threats with minimal training data. The ability of LLMs to quickly adapt and generalize could be a game-changer in the ongoing battle against online fraud.