Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights

Abstract

The number of disclosed vulnerabilities has been steadily increasing over the years. At the same time, organizations face significant challenges patching their systems, leading to a need to prioritize vulnerability remediation in order to reduce the risk of attacks. Unfortunately, existing vulnerability scoring systems are either vendor-specific, proprietary, or are only commercially available. Moreover, these and other prioritization strategies based on vulnerability severity are poor predictors of actual vulnerability exploitation because they do not incorporate new information that might impact the likelihood of exploitation. In this paper we present the efforts behind building a Special Interest Group (SIG) that seeks to develop a completely data-driven exploit scoring system that produces scores for all known vulnerabilities, that is freely available, and which adapts to new information. The Exploit Prediction Scoring System (EPSS) SIG consists of more than 170 experts from around the world and across all industries, providing crowd-sourced expertise and feedback. Based on these collective insights, we describe the design decisions and trade-offs that lead to the development of the next version of EPSS. This new machine learning model provides an 82% performance improvement over past models in distinguishing vulnerabilities that are exploited in the wild and thus may be prioritized for remediation.