# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction
### Comparison of Related Surveys
They provided a detailed analysis of the development and research relationships of technologies such as UAV antennas, obstacle detection, flight control, and the Internet of Things (IoT). Nevertheless, they lacked an analysis focused on UAV security. Moreover, in 2024, [PERSON] et al. provided a detailed overview of UAV system architectures and explored the security threats targeting UAV systems [45]. They emphasized the need for RL-based UAV security solutions for each type of attack. However, this work focuses more on UAV security and lacks a discussion of UAV safety. In 2024, [PERSON] et al. reviewed the security challenges of UAVs, including jamming attacks, search attacks, spoofing attacks, electromagnetic attacks, and deauthentication attacks, as well as related security control measures [46]. They also emphasized the role of AI technologies in UAV safety and security. In 2024, [PERSON] et al. explored UAV-assisted IoT, discussing key issues such as the characteristics of UAVs and the IoT, communication complexity, self-healing and self-organizing capabilities, random communication changes, and cybersecurity threats [47]. They highlighted the potential of ML, DL, and RL approaches in enhancing the security of UAV-assisted IoT. However, these papers also lack discussion on UAV safety. Although AI techniques have demonstrated strong application potential in the field of UAV security, the limitations of traditional AI approaches in UAV safety and security have been explicitly noted [48]. Although advances have been made in recognition capabilities, detection algorithms are still sensitive to adversarial attacks, and model accuracy still requires improvement. In addition, it is necessary to ensure, through other means, that data analysis conducted through ML algorithms can fully protect the privacy and security of UAV data. The emergence of generative artificial intelligence (GAI) and LLM technologies offers promising avenues to address some of these limitations. In 2023, [PERSON] et al. indicated that the integration of a GAI or LLMs with UAV security will likely become a standard trajectory in future research [44; 49]. In 2024, [PERSON] et al. reported that GAI methods could contribute to generating UAV communication training data, optimizing communication models, reconstructing environments, and determining the location of UAVs in the absence of global positioning system (GPS) signals by interpreting radio signals [50]. In 2024 and 2025, [PERSON] et al. and [PERSON] et al. combined LLMs with UAV network design and visual recognition, demonstrating the potential of LLMs in the UAV field [10; 17]. However, they did not directly mention UAV security. In 2024, [PERSON] et al. and [PERSON] et al. thoroughly discussed the classification of GAI methods and summarized the roles and limitations of advanced GAI models, such as generative adversarial networks (GANs), autoencoders, variational autoencoders (VAEs), and diffusion models (DMs), in addressing physical layer security challenges, including communication confidentiality, authentication, availability, resilience, and integrity [9; 49]. They also noted the potential of GAI methods in addressing the sparsity and incompleteness of physical layer security data, providing heuristic ideas and novel insights. A comparison of related surveys is shown in Table 1.  
Prior surveys on AI applications in UAV safety and security mainly summarize existing technologies or provide taxonomies of AI methods. Our work introduces a novel framework, classifying UAV safety and security threats into five distinct aspects, each based  
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