Cyberattacks are becoming more sophisticated, and organizations are constantly under threat from various types of security breaches. To protect against these threats, it is essential to identify the vulnerability and impact of these weaknesses and address them before attackers can exploit them. However, manually identifying and characterizing vulnerability can be a time-consuming and tedious process that adds to the workload of cybersecurity experts. To address this challenge, this paper presents a doctoral research proposal to automate the process of identifying novel technologies, including learning-based technologies, to infer vulnerabilities from a text about an attack. In addition, this paper uses natural language processing techniques to extract relevant information from attack text and analyze repositories for known vulnerabilities. This paper presents an in-depth analysis of the research challenges and goals to understand how innovative technologies can be used to detect and identify vulnerabilities in text about attacks. It also covers the preliminary work done, literature review findings, and threats to validity.