The increasing integration of machine learning (ML) approaches into the operation and management (O&M) of modern networks has led researchers to address various problems such as performance optimization, anomaly detection, traffic prediction, root-cause analysis and incident troubleshooting. Autonomous networks leverage the wealth of both business and operations data to achieve fully intelligent and automated O&M for various telecommunications applications. However, their high level of service requires the closest scrutiny as such applications depend on their resilience and trustworthiness, especially in the face of motivated attackers that aim at abusing their underlying ML models. This workshop fosters the close collaboration between researchers and practitioners at the intersection of security, networks and ML communities to improve the security of ML applications in autonomous networks together.