[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82773-en":3,"doc-seo-82773-105":28,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},82773,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities","The document presents advanced topic modeling methods to analyze and prioritize software vulnerabilities in the face of growing complexity and abundant unstructured threat text. It applies LLM-powered topic modeling—BERTopic, Top2Vec, CombinedTM, and Llama2 paired with BERTopic, plus Mixtral—using dimensionality reduction and clustering such as UMAP, PCA, HDBSCAN, and DBSCAN. Latent patterns and interpretable clusters improve threat prioritization, enabling scalable, automated vulnerability-management support and better cybersecurity decision-making.","Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities  \nUtkarsh Tiwari 1 , Spoorthi M2 , Anirudh S3 , and Nidhin Prabhakar T. V.4*  \nDepartment of Computer Science & Engineering,  \nAmrita School of Computing, Bengaluru,  \nAmrita Vishwa Vidyapeetham, India  \n[1](1 bl.en.u4cse21212@bl.students.amrita.edu)[ bl.en.u4cse21212@bl.students.amrita.edu](1 bl.en.u4cse21212@bl.students.amrita.edu), [2](2 bl.en.u4cse21193@bl.students.amrita.edu)[ bl.en.u4cse21193@bl.students.amrita.edu](2 bl.en.u4cse21193@bl.students.amrita.edu),  \n[3](3 bl.en.u4cse21020@bl.students.amrita.edu)[ bl.en.u4cse21020@bl.students.amrita.edu](3 bl.en.u4cse21020@bl.students.amrita.edu),4* tv [nidhin@blr.amrita.edu](nidhin@blr.amrita.edu)  \narXiv :2607 .03887v 1 [ cs .CR] 4 Jul 2026  \nAbstract—The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages state-of-theart topic modeling techniques powered by large language models (LLMs) to extract meaningful insights from the ’Threat’ feature of a software vulnerability dataset. Models such as BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, and Mixtral are utilized, along with dimensionality reduction and clustering methods like UMAP, PCA, HDBSCAN, and DBSCAN. By uncovering latent patterns and generating interpretable clusters, this research enhances threat prioritization and decision-making in cybersecurity. The findings support scalable and automated solutions for vulnerability management, contributing to improved security practices.  \nIndex Terms—Software Vulnerabilities, Topic Modeling, Large Language Models, Cybersecurity, BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, Mixtral, Dimensionality Reduction, Clustering  \nI. INTRODUCTION  \nSoftware vulnerabilities pose a threat to the security of various organizations which may lead to financial, reputational, and operational risks. With time, the volume and complexity of vulnerabilities grow, and it is necessary to identify and categorize them effectively. Identifying and categorizing these vulnerabilities effectively has become a major challenge for organizations. This paper addresses this gap by employing stateof-the-art topic modeling techniques to analyze and categorize vulnerabilities.  \nThe proposed workflow starts off with preprocessing the dataset to ensure data compatibility. Five advanced topic modeling approaches—BERTopic with multiple configurations, CombinedTM, Top2Vec, Llama2 with BERTopic, and mixtral 8x7b—are applied to extract latent topics. Comparative analysis evaluates the performance of each model using metrics like topic coherence and clustering quality, supported by visualizations to aid interpretability. This paper benefits various cybersecurity researchers, software developers, and organizations by providing actionable insights into recurring vulnerability themes. The contributions of this paper involve:  \n• Application of BERTopic with Multiple Configurations: The paper explores four unique configurations of BERTopic using UMAP, PCA, DBSCAN, and advanced  \nlanguage embeddings, demonstrating the adaptability of the framework in identifying meaningful patterns.  \n• Incorporation of CombinedTM and Top2Vec: CombinedTM aligns Bag-of-Words representations with contextual embeddings, while Top2Vec efficiently identifies topics by embedding documents and words into a shared semantic space without iterative optimization.  \n• Integration of Large Language Models (LLMs): Models such as Llama2 with BERTopic and mixtral 8x7b are used for contextual topic labeling and document-specific topic generation, enhancing the interpretability of results.  \nMoving on, the paper is structured in such a way that Section 2 presents the Literature Survey, Section 3 talks about the proposed methodology, ","cbCaidfxm2639j7S","https://ap.wps.com/l/cbCaidfxm2639j7S","pdf",774553,1,"English","en",105,"# I. 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