Man-in-the-Browser (MitB) attacks represent a sophisticated class of web-based threats that manipulate browser functionality to intercept and modify user transactions in real-time. Traditional server-side detection mechanisms often fail to identify these attacks due to their client-side nature and encrypted communication channels. This paper presents a novel client-side runtime integrity agent that employs forensic monitoring and machine learning-based anomaly detection to identify MitB attacks at their source. The proposed system integrates DOM integrity verification, memory forensic analysis, and behavioral pattern recognition to detect malicious browser modifications before they can compromise user sessions. Our experimental evaluation demonstrates a detection accuracy of 97.3% with a false positive rate of 2.1%, significantly outperforming existing client-side detection methods. The system successfully identified various MitB attack vectors, including Zeus, SpyEye, and custom injection payloads, while maintaining a minimal computational overhead of less than 3% CPU utilization.