## 1 Introduction
However, traditional CVSS scoring methods often rely on static rules and manual interpretation, which can lead to inconsistencies, oversimplifications, or delays in rapidly evolving threat landscapes. This static nature is particularly problematic in the dynamic and safety-critical automotive domain, where highly interconnected cyber-physical systems, heterogeneous components, and the rapid pace of technological advancements introduce complex, often context-dependent, vulnerabilities that traditional CVSS struggles to accurately assess and prioritize in real time. In addition, traditional CVSS often fails to adapt to evolving threats, leading to the need for more advanced solutions. Therefore, utilizing automated solutions seems essential, also considering that they can provide an estimate of the severity [19; 20]. Today, generative AI represents a transformative subset of artificial intelligence that focuses on creating new content or solutions by learning patterns from existing data. This innovation contrasts with traditional AI, which mainly deals with classification, prediction, and regression tasks [21]. In the context of cybersecurity, generative AI holds promise in improving vulnerability management practices [22]. Crucially, its capacity to analyze vast datasets, learn dynamic threat patterns, and generate context-aware insights makes it exceptionally well-suited for addressing the unique and intricate cybersecurity challenges within complex automotive systems. Generative AI can improve automated vulnerability scoring by analyzing historical data to forecast potential vulnerabilities and adjusting severity scores based on dynamic risk indicators [23]. This shift allows organizations to prioritize vulnerabilities based on actual organizational impact rather than just raw numbers, optimizing remediation efforts and resource allocation. Additionally, generative AI can assist in vulnerability detection by refining the process and reducing false positives. By analyzing service configurations and detection mechanisms, AI can improve the precision of identifying legitimate vulnerabilities. As generative AI continues to evolve, its application in cybersecurity presents an opportunity to create more resilient and efficient systems for vulnerability management, ultimately improving organizational security in an increasingly complex threat landscape. Addressing the critical need for a more dynamic, automated, and context-aware approach to vulnerability assessment in autonomous vehicles, especially given the limitations of traditional CVSS in this highly complex and safety-critical domain, this paper addresses this gap by proposing a hybrid generative AI model for enhanced automated vulnerability scoring tailored to autonomous vehicles. Our contributions are as follows:  
* Development of a sensor-centric vulnerability taxonomy tailored to autonomous vehicles, identifying and categorizing over 15 unique attack types. The taxonomy highlights LiDAR and radar systems as the most frequently targeted components, due to their critical role in autonomous perception and control. * Application of generative AI models for context-aware vulnerability scoring to evaluate and utilize state-of-the-art generative AI platforms (e.g., ChatGPT, Gemini, Copilot, and DeepSeek) to enhance automated scoring by incorporating contextual factors and dynamic threat indicators often missed by static methods. * Development of a hybrid vulnerability scoring model to integrate expert-driven knowledge with generative AI outputs to adapt and extend CVSS v4.0 for the specific context of autonomous vehicle sensor systems. The remainder of this paper is organized as follows: Section 2 presents the background and related work. Section 3 outlines the methodology of this work. Section 4 introduces a comprehensive sensor-centric vulnerability taxonomy tailored to autonomous vehicle systems, highlighting over 15 unique attack types.