One way to protect users from clicking on a malicious URL is to continuously check all URLs displayed on the website and notify them when a suspicious URL is detected. This paper presents a browser plug-in to detect malicious web addresses facilitating phishing attacks. The plug-in leverages a machine-learning model, specifically the Extreme Gradient Boosting decision tree model. The results indicate high performance in accurately identifying malicious URLs. Although the XGBoost model does not achieve the highest possible accuracy, it offers an exceptional balance between various performance metrics. It provides practical benefits in terms of computational efficiency and interpretability. These features make it a solid foundation for further development and potential implementation in phishing detection systems on social networking sites. The plug-in identifies and flags all external URLs on a given page, providing users with information regarding the potential maliciousness of a URL.