# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction  
### Motivation  
Unmanned aerial vehicles (UAVs) have demonstrated significant application value across multiple fields because of their exceptional autonomy, flexibility, and adaptability. Low-altitude flight activities in various scenarios, such as package delivery [1], agriculture [2], fire detection [3], and other operational tasks, have led to the development of comprehensive economic models that integrate and support advancements across related fields. However, as the number of deployed UAVs increases and their missions diversify, UAV safety and security issues have become increasingly complex. Potential social problemsassociated with UAVs include flight disruptions, collision or crash incidents, unauthorized flights [4], and no-fly zone designations. In a complex and three-dimensional low-altitude traffic environment, large-scale UAV operations may encounter various dynamic and complex obstacles and threats, including high-risk possibilities of collisions between aircraft and buildings or other obstacles [5]. Such incidents may seriously threaten airspace order, public property, and even personal safety. The complexity of these issues underscores the need for technical solutions that consider physical safety and network security. To address these challenges, artificial intelligence (AI) technologies have been applied to expand UAV applications, particularly in areas such as image recognition and target tracking [6]. Moreover, AI technology has become the core support for UAV safety and security. For example, deep reinforcement learning (DRL), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks can be employed to optimize UAV resource allocation, latency estimation, and energy management effectiveness [7]. In terms of environmental sensing and decision-making, the combination of convolutional neural networks (CNNs) and distributed reinforcement learning (RL) has supported improvements in obstacle avoidance and the route planning efficiency of UAV swarms in complex scenarios [8]. In threat detection and cybersecurity, LSTM has been used to analyze sensor data for mechanical failure and network intrusion prediction. However, the limitations of AI algorithms, including machine learning (ML) algorithms, constrain their broader deployment and application. For example, traditional AI algorithms still require further improvement in rapidly changing environments to ensure the stability of their output results. The output accuracy of many data-driven ML models is highly dependent on the availability of large amounts of high-quality training data. However, acquiring such data can be difficult due to data privacy restrictions and regional regulatory limitations, which significantly affect the output accuracy of ML models, especially in critical safety and security applications. To address these limitations, large language models (LLMs) present a promising alternative. LLMs can enhance model stability in dynamic environments by supporting contextual understanding and high-level reasoning, which can help UAVs adapt more effectively to changes. Moreover, by leveraging the vast knowledge and natural language understanding capabilities of LLMs, UAVs can make more informed decisions with less reliance on extensive, high-quality training data, which is particularly crucial for critical UAV safety and security applications where data availability is limited. Moreover, LLMs facilitate natural language interactions between operators and UAVs, allowing real-time adjustments and interventions to ensure safety and security. In light of these potential benefits, researchers have begun exploring the integration of LLMs with UAV systems to increase their performance and capabilities. Recently, in 2024, [PERSON] et al. explored the integration of LLMs and UAV communication systems [9]. In 2024, [PERSON] et al.