How to Poison an xApp: Dissecting Backdoor Attacks to Deep Reinforcement Learning in Open Radio Access Networks

The development of Open Radio Access Network (RAN) cellular systems is being propelled by the integration of Artificial Intelligence (AI) techniques. While AI can enhance network performance, it expands the attack surface of the RAN. For instance, the need for datasets to train AI algorithms and the use of open interface to retrieve data in real time paves the way to data tampering during both training and inference phases. In this work, we propose MalO-RAN, a framework to evaluate the impact of data poisoning on O-RAN intelligent applications. We focus on AI-based xApps taking control decisions via Deep Reinforcement Learning (DRL), and investigate backdoor attacks, where tampered data is added to training datasets to include a backdoor in the final model that can be used by the attacker to trigger potentially harmful or inefficient pre-defined control decisions. We leverage an extensive O-RAN dataset collected on the Colosseum network emulator and show how an attacker may tamper with the training of AI models embedded in xApps, with the goal of favoring specific tenants after the application deployment on the network. We experimentally evaluate the impact of the SleeperNets and TrojDRL attacks and show that backdoor attacks achieve up to a 0.9 attack success rate. Moreover, we demonstrate the impact of these attacks on a live O-RAN deployment implemented on Colosseum, where we instantiate the xApps poisoned with MalO-RAN on an O-RAN-compliant Near-real-time RAN Intelligent Controller (RIC). Results show that these attacks cause an average network performance degradation of 87%.