The integration of artificial intelligence (AI) and machine learning (ML) within the Open Radio Access Network (O-RAN) xApps introduces significant enhancements to network automation and anomaly detection. However, this open architecture increases vulnerability to sophisticated attacks, including backdoor threats. This paper proposes the use of an xLSTM autoencoder for detecting anomalies in O-RAN, specifically focusing on its ability to model long-term dependencies in network traffic. xLSTM, with its enhanced memory mechanisms, addresses the limitations of traditional models like LSTM by improving both detection accuracy and computational efficiency in real-time environments.