Ongoing advances in digitization and automation of critical infrastructures, particularly in terms of aeronautical Communication, Navigation, and Surveillance (CNS) technologies, has led to the fusion of intricate physical and information networks with artificial intelligence (AI) systems. This integration is empowered avionics and Air Traffic Management (ATM) systems with enhanced data processing capabilities, interactive information exchange, and expansive geographic distribution. However, these advancements also expose the systems to increasing cybersecurity threats, physical vulnerabilities, and data integrity risks. The complexity and interconnectedness of CNS infrastructure, combined with ATM systems, amplify the potential scope and depth of attacks and increase their ability to spread through interconnected components. As a result, both ATM and UAS Traffic Management (UTM) systems face an escalation in security challenges. While the concept of cybersecurity within aviation has a long history, its seamless integration into aviation systems remains a significant challenge. Just as AI technology is harnessed to improve the overall operational efficiency and reliability of aviation systems, it has also emerged as a pivotal battleground for cybersecurity risks and threats. Increasingly, AI-driven intrusion and theft methods are replacing traditional approaches. In response, researchers have put forth defensive strategies rooted in AI technology. This paper critically evaluates cybersecurity vulnerabilities and threats that may confront ATM and UTM systems. It systematically categorizes a variety of potential threat actors along with their corresponding targets, based on their objectives, motivations, and capabilities. Simultaneously, the paper delves deeply into an exploration of possible attack methodologies founded on AI technology, accompanied by their corresponding defensive tactics.