  Open Radio Access Networks (O-RAN) are emerging as a disruptive technology,
revolutionising traditional mobile network architecture and deployments in the
current 5G and the upcoming 6G era. Disaggregation of network architecture,
inherent support for AI/ML workflows, cloud-native principles, scalability, and
interoperability make O-RAN attractive to network providers for beyond-5G and
6G deployments. Notably, the ability to deploy custom applications, including
Machine Learning (ML) solutions as xApps or rApps on the RAN Intelligent
Controllers (RICs), has immense potential for network function and resource
optimisation. However, the openness, nascent standards, and distributed
architecture of O-RAN and RICs introduce numerous vulnerabilities exploitable
through multiple attack vectors, which have not yet been fully explored. To
address this gap and ensure robust systems before large-scale deployments, this
work analyses the security of ML-based applications deployed on the RIC
platform. We focus on potential attacks, defence mechanisms, and pave the way
for future research towards a more robust RIC platform.
