## 6 Conclusions  
This study examined the security implications of GAI by analyzing 1047 academic articles sourced from the SCOPUS database. Using scientometric methods, including TF-IDF analysis, keyword centrality analysis, and LDA topic modeling, we identified key trends, themes, and security challenges associated with GAI technologies, particularly in relation to systems like ChatGPT. Furthermore, the evolution of scientific production in this field demonstrates a rapid increase in research interest, with only 2 publications in 2022, followed by a sharp rise to 540 publications in 2023 and 504 publications in the first half of 2024 (as of June). This trend highlights the growing significance of GAI security research, underscoring the need for continuous investigation into emerging threats and robust security frameworks. The findings reveal that GAI security research is a global effort, with significant contributions from countries such as the United States, China, and India. Leading institutions, including the Chinese Academy of Sciences and the National University of Singapore, are at the forefront of advancing this field. Our analysis also highlighted the centrality of termslike \"ChatGPT\", \"AI\", and \"model\" in current research, reflecting the prominence of large language models and AI systems in discussions of security. The six themes identified through LDA topic modeling--ranging from AI in education to healthcare security--illustrate the diverse applications and associated risks of GAI. While research on AI in education and language models remains dominant, emerging fields such as healthcare security are receiving increasing attention and require further exploration to comprehensively assess and mitigate potential risks in sensitive domains. Despite the valuable insights that this study provides, it has several limitations. Firstly, the dataset is confined to articles published in SCOPUS, which, although comprehensive, may not encompass all relevant research, particularly non-English literature or studies from less prominent journals. This may introduce a regional or perspective-based bias in the findings. Additionally, the study's data collection period (June 2022 to June 2024) captures recent developments but excludes earlier foundational research on AI security. Given the rapid pace of GAI advancements, the conclusions drawn may become outdated as new technologies and challenges emerge. Furthermore, while LDA topic modeling offers a structured view of research trends, this method relies on statistical probabilities, which may not fully capture the depth and nuances of each article's content. A more in-depth qualitative analysis could complement these findings by providing richer insights into the contextual details of GAI security challenges. In conclusion, as GAI technologies continue to evolve, developing comprehensive security frameworks is essential to address both technical vulnerabilities and broader ethical and societal challenges. Future research should focus on interdisciplinary approaches that integrate technical, legal, and ethical perspectives to ensure the safe and responsible deployment of GAI technologies across industries. Additionally, expanding the analytical scope through the incorporation of diverse data sources and the adoption of a hybrid quantitative-qualitative approach will provide a more holistic and nuanced understanding of emerging security challenges associated with GAI systems. Methodology, M.N. and Y.S. ; Project Administration, J.L. ; Resources, B.K. ; Software, K.J. ; Supervision, M.C. ; Writing--Original Draft, K.J. ; Writing--Review and Editing, J.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Policy Expert Development and Support Program through the Ministry of Science and ICT of the Korean government, grant number S2022A066700001.