LLM Fine-Tuned Threat Intelligence Summarization Agent for CVE Report Automation
In this paper, an intelligent cyber threat intelligence framework involving automated vulnerability severity assessment, contextual risk interpretation and generation of mitigation recommendation is presented. The proposed system has been designed to analyze the CVE-related description of vulnerabilities and the security metadata related to them, classify the level of severity of the threat and estimate its relevance to risk with the help of a transformer-based natural language processing model. To build contextual awareness beyond classification, it adds a retrieval-augmented mechanism to recognize semantically similar vulnerability records for contextual evidence-based threat interpretation. It is additionally fortified with vulnerability analysis, like CVE retrieval, client qualifications, record following, and even visualisation as a web application platform. The two processes, one involving the severities of the transformers, and the other the retrieval of threat intelligence and mitigation advice, into a single operational flow, thus reducing the manual reliance on Vulnerability Triage and aiding security analysts in prioritizing cyber risks. The proposed framework allows for the automatic processing of textual information on vulnerabilities and the comparison of such information and a contextual analysis with previous vulnerabilities discovered. Unlike conventional vulnerability assessment approaches that perform severity classification independently of contextual threat interpretation, the proposed framework integrates transformer-based semantic analysis, retrieval-augmented vulnerability intelligence, cyber-risk estimation, and mitigation recommendation generation within a unified analytical workflow. By combining predictive language modelling with contextual vulnerability retrieval, the framework supports evidence-driven cyber threat analysis and structured decision support for security analysts. The proposed architecture provides a scalable approach for automated vulnerability prioritization and contextual cyber threat intelligence that is suitable for modern cybersecurity operations involving large volumes of vulnerability reports.