# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction
### State of the Art  
UAV safety and security have become core concerns in both academia and industry, each with distinct focuses and approaches. There are surveys that categorize this issue into two categories: safety and security [18; 19]. Safety pertains to the physical integrity of UAV  
Figure 1: Structure of this paper. hardware and the prevention of risks to human safety from incidents such as collisions or crashes. This aspect emphasizes the reliability of components, such as structural integrity and stability, ensuring that mechanical failures are minimized to avoid operational compromise or threats to human life. Conversely, while safety focuses on maintaining physical reliability and preventing harm, security is concerned with protecting UAV systems from intentional disruptions to data and control. Security involves safeguarding UAV networks and communications against threats such as unauthorized access and cyberattacks. It depends on robust encryption techniques and resilient network management strategies to ensure secure communication and command integrity. Together, these two domains form the foundation of UAV safety and security research, jointly addressing the multifaceted challenges associated with UAV deployment across diverse application scenarios. UAV safety, a cornerstone of hardware reliability, has undergone significant research advancements aimed at enhancing resilience against physical threats. In 2022, [PERSON] et al. explored collision safety for lightweight UAVs and recommended integrated active and passive safety systems to mitigate real-world risk [20]. In 2023, [PERSON] et al. further classified physical attacks on internet of unmanned aerial vehicles (IoUAVs), underscoring the need for robust countermeasures. Current trends show a shift from passive detection to active defenses, leveraging technologies such as multisensor data fusion and AI-driven anomaly detection [21]. In 2024, [PERSON] et al. analyzed vulnerabilities in flight controllers, sensors, and airframes and proposed hardware-specific security measures to counter side-channel attacks [22]. Efforts have been made to increase sensor reliability through multisensor data fusion and encrypted communication links to counteract vulnerabilities such as spoofing attacks [23]. Additionally, innovations in UAV physical safety have been reported, with approaches involving neural network-based systems for predicting battery life and AI-driven self-healing materials to address potential damage [24; 25]. More recently, the integration of LLMs and DRL has revolutionized real-time obstacle detection and autonomous navigation, enabling UAVs to adapt dynamically to complex environments [26]. These advancements collectively enhance UAV safety and reliability in diverse scenarios. UAV security research has also become increasingly critical, with a focus on safeguarding network communication integrity. Traditional approaches focus on cryptographic methods and essential sensor protection to ensure communication integrity and environmental awareness. However, as threats evolve, research has shifted toward integrating AI algorithms like ML techniques to increase robustness. For example, AI-driven multifactor authentication and lightweight cryptographic algorithms were developed to address dynamic environments and resource constraints [27; 28]. More recently, emphasis has been placed on leveraging LLMs and deep learning (DL) for real-time threat detection and adaptive defense mechanisms. LLMs are now used to generate lightweight authentication policies, predict interference sources, and optimize frequency-hopping strategies, significantly improving UAV resilience against electromagnetic interference and spoofing attacks [29]. Additionally, multimodal data fusion approaches incorporating LLMs have supported enhanced UAV sensing capabilities, contributing to more accurate detection of anomalies and improved situational awareness [17].