Item compromise has long posed challenges in educational measurement, jeopardizing both test validity and test security of continuous tests. Detecting compromised items is therefore crucial to address this concern. The present literature on compromised item detection reveals two notable gaps: First, the majority of existing methods are based upon a non-Bayesian framework; second, many of these approaches exclusively rely on examinees' responses for detection, neglecting valuable data such as response times. In this study, we propose a Bayesian change-point method that integrates both responses and response times to detect compromised items in continuous tests. This two-phase approach is designed for iterative use. The accuracy and efficiency of the proposed method are assessed in three simulations and an operational data example. The results demonstrate the method's effectiveness in accurately and efficiently detecting compromised items. Additionally, the incorporation of response times significantly enhances both detection accuracy and efficiency.