The field of cybersecurity is moving towards more proactive and predictive approaches to vulnerability analysis and threat detection. Researchers are exploring the use of machine learning and large language models to improve the accuracy and efficiency of vulnerability assessments and threat predictions. A key area of focus is the development of frameworks and models that can predict the impact of cybersecurity vulnerabilities and map them to relevant techniques and tactics. Another important direction is the use of graph-theoretic methods to measure lateral movement susceptibility and estimate blast radius in ransomware attacks. Noteworthy papers in this area include:

• KillChainGraph, which presents a phase-aware, multi-model machine learning framework for predicting and mapping ATT&CK techniques.
• TRIAGE, which introduces a two-pronged automated approach using large language models to map CVEs to relevant techniques from the ATT&CK knowledge base.
• Measuring Ransomware Lateral Movement Susceptibility via Privilege-Weighted Adjacency Matrix Exponentiation, which presents a graph-theoretic method to measure lateral-movement susceptibility and estimate blast radius.