## 2 Background and Related Work
### Related Work  
Building upon this general overview of generative AI's role and potential across various cybersecurity domains, the following section reviews specific existing literature focusing on AI's applications in vulnerability management and the Common Vulnerability Scoring System (CVSS), which directly informs our proposed methodology. Generative AI has emerged as a transformative tool in the realm of vulnerability management, enhancing the capabilities of automated vulnerability scoring systems. By leveraging advanced machine learning techniques, organizations can improve the efficiency and accuracy of their vulnerability assessments. LLMs have significantly advanced generative AI tasks, becoming central to applications such as text generation, translation, and summarization. However, their widespread use has brought increasing concern over security vulnerabilities that could compromise the integrity and reliability of their outputs. Two of the most critical threats identified are prompt injection and training data poisoning attacks, which can lead to biased results, the spread of misinformation, and the generation of malicious content. To address these issues, the work in [38] proposes extending the CVSS framework to assess the severity of LLM-specific vulnerabilities. This approach provides a standardized method for evaluating and understanding these risks, allowing organizations to prioritize mitigation strategies, allocate resources effectively, and implement targeted security measures to protect against potential threats. The work in [20] addresses the challenge of manually assessing newly published vulnerabilities using the CVSS, which is time-consuming and requires expertise. Previous attempts to predict CVSS vectors or scores using machine learning have typically relied on textual descriptions from databases like the National Vulnerability Database (NVD). In the proposed work, the authors expand on this approach by incorporating additional publicly available web pages referenced by the NVD through web scraping. A Deep Learning-based method is developed and evaluated for predicting CVSS vectors. The paper classifies the reference texts based on their suitability and crawlability. Although the inclusion of these additional texts has a negligible impact, the Deep Learning model outperforms existing methods in predicting CVSS vectors, improving the efficiency of vulnerability assessment. In [19], the authors explore the use of artificial intelligence models to assist or replace human experts in evaluating the severity of vulnerabilities, given the large volume of vulnerabilities that need assessment. The authors compare the performance of the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine models, trained on 118,000 vulnerabilities and tested on 51,000, against human experts in terms of precision, recall, F1 score, and mean estimation error. The Universal Sentence Encoder outperforms human experts with 72-77% accuracy in predicting severity levels, showing high efficiency in memory usage and processing time. The study further evaluates the models' effectiveness in predicting vulnerability evaluation components and severity levels. The findings demonstrate the potential of AI to significantly enhance the efficiency and accuracy of vulnerability severity assessments, which is currently a fully manual process. The work in [22] addresses the limitations of traditional vulnerability assessment methods, which rely on expert knowledge and are time-consuming. Given the increasing number of vulnerabilities, the authors propose using machine learning (ML) solutions for more efficient vulnerability severity assessment. The paper focuses on improving existing ML-based methods that predict the CVSS score or its vector metrics.