###### Abstract
Malicious or compromised xApps and rApps could perform privilege escalation or model manipulation or disrupt key RIC functionalities [11]. Such vulnerabilities could lead to severe consequences, including service interruptions, unauthorized access to sensitive user data, and even large-scale network outages. For example, a compromised xApp responsible for handover management could deliberately delay handovers, causing dropped calls or degraded service quality, which directly impacts user experience and revenue for network operators [11]. Additionally, the integration of ML into mission-critical decision loops opens the door to adversarial attacks, such as poisoning the training process, inferring sensitive information, or degrading the inference accuracy via evasion tactics [12]. Recent studies have demonstrated how adversarial ML attacks can manipulate model outputs in real-world scenarios. For instance, researchers showed that injecting malicious gradients during FL could degrade the accuracy of a global model by up to 40%, severely impacting its reliability [13]. Similarly, membership inference attacks have been shown to expose sensitive user patterns, violating privacy and potentially leading to regulatory penalties under frameworks like GDPR [14]. Moreover, current O-RAN security models often rely on coarse-grained trust assumptions--e.g., treating all internal components as trusted once authenticated. This violates the principles of the Zero Trust Architecture (ZTA), which advocates continuous verification, least-privilege access, and contextual awareness across the entire infrastructure [15]. Without ZTA principles, unauthorized lateral movement or stealthy data exfiltration becomes feasible, especially in distributed and dynamic environments like O-RAN. For example, the lack of fine-grained access controls in traditional RAN architectures has been exploited in past incidents [16], where attackers moved laterally to access sensitive customer data [10]. Such incidents underscore the importance of adopting ZTA to mitigate similar risks in O-RAN. Specific studies on O-RAN vulnerabilities have highlighted the risks associated with open interfaces. For instance, a recent study provides an in-depth examination of the security aspects of the E2 interface within the O-RAN architecture [17]. The E2 interface plays a critical role in enabling communication between the RAN Intelligent Controller (RIC) and the E2 node, but it also introduces diverse vulnerabilities and potential security threats. The study highlights how attackers could exploit these vulnerabilities to inject rogue xApps or manipulate network behavior, potentially leading to service disruptions. Furthermore, the paper reviews the security mechanisms proposed by the O-RAN Alliance to mitigate these risks, emphasizing the need for robust safeguards to protect this crucial component of the network. The findings underscore the importance of addressing E2 interface security to ensure the overall resilience and trustworthiness of O-RAN deployments. Similarly, a study by Nokia Bell Labs analyzed potential threats in O-RAN deployments, emphasizing the risk of untrusted third-party xApps bypassing authentication mechanisms and accessing sensitive network data [10]. These findings reinforce the need for enhanced security measures, such as runtime attestation and continuous monitoring, to safeguard O-RAN environments. Lastly, while FL helps mitigate privacy concerns, it is not immune to attacks. Gradient leakage and membership inference attacks can expose sensitive user patterns or disrupt the training process. Differential Privacy (DP) offers theoretical guarantees for privacy preservation but may incur trade-offs in accuracy and latency, making its integration into real-time RIC systems non-trivial [18]. These challenges motivate the development of a unified security and trust framework tailored for O-RAN, combining the rigor of ZTA with the privacy-preserving benefits of FL.