## 2 Related Work
These methods enhance the review process by offering a structured, data-driven approach to understanding the relationships between research papers through graphical representation. However, while text mining can identify patterns in research, it often lacks the granularity needed to capture the nuanced, topic-specific context of these studies [26]. To bridge this gap, latent Dirichlet allocation (LDA) topic modeling has emerged as a more advanced technique for discovering and mining latent information within a research domain. LDA topic modeling utilizes statistical and optimization algorithms to extract semantic insights from large text corpora, making it a valuable tool for systematically analyzing textual data [27]. This method has been widely applied across various fields, including scientific research trends, social networks, political discourse, biomedical recommendations, and public health initiatives [25]. In technology and cybersecurity, LDA topic modeling is particularly useful for identifying patterns in cybersecurity incidents, studying public perceptions of privacy, and uncovering emerging trends in artificial intelligence research. Several recent studies have explored the security implications of large language models (LLMs) and generative AI. [PERSON] et al. (2023) conducted a broad review of adversarial attacks on LLMs, focusing on how AI-generated text can be manipulated for misinformation and phishing attacks [18]. [PERSON] and [PERSON] (2024) examined privacy risks associated with LLMs, including data leakage and user profiling concerns. [PERSON] et al. (2023) analyzed LLMs' robustness, assessing their resilience against adversarial prompts and bias exploitation [18,19]. [PERSON] et al. (2023) reviewed regulatory and ethical frameworks surrounding LLM security, emphasizing policy recommendations rather than technical vulnerabilities [23]. A significant body of research has also focused on the application of text mining, including LDA topic modeling, to provide overviews of scientific papers and uncover research topics. These studies can be broadly categorized into two groups [27]: In the first category, text mining is employed to provide broad overviews across a wide range of research areas. For instance, [PERSON] et al. used unsupervised text mining techniques to analyze historical trends in computational linguistics [28], while [PERSON] et al. applied LDA topic modeling to cluster scientific documents and evaluate their findings [29]. The second category focuses on identifying trends within specific fields through text mining. For example, [PERSON] et al. conducted a bibliometric analysis using text mining to evaluate academic publications on generative AI in education, which analyzed research trends, collaboration networks, sentiment analysis, and topic modeling. Their study identified the evolution of AI-related terms, a growing positive sentiment in academic discourse, and emerging research themes, such as personalized learning, AI-driven gamification, and hybrid human-AI classrooms [30]. Similarly, [PERSON] et al. used text mining to analyze document abstracts and identify trends in personal data privacy research [31]. More recently, [PERSON] et al. applied structural text network analysis to study articles on maritime autonomous surface ships (MASSs), identifying key trends in this rapidly evolving field [32]. However, despite these contributions, existing studies on LLM security have primarily adopted qualitative, conceptual approaches, often lacking systematic mapping of the research landscape through scientometric methods. Many prior reviews have explored security challenges in LLMs, focusing on adversarial attacks, privacy risks, bias exploitation, and regulatory considerations. While these studies provide valuable insights into distinct security threats, they primarily synthesize existing knowledge without offering a quantitative, data-driven analysis of research trends.