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Assessing the exploitability of software vulnerabilities at the time ofdisclosure is difficult and error-prone, as features extracted via technicalanalysis by existing metrics are poor predictors for exploit development.Moreover, exploitability assessments suffer from a class bias because "notexploitable" labels could be inaccurate. To overcome these challenges, wepropose a new metric, called Expected Exploitability (EE), which reflects, overtime, the likelihood that functional exploits will be developed. Key to oursolution is a time-varying view of exploitability, a departure from existingmetrics. This allows us to learn EE using data-driven techniques from artifactspublished after disclosure, such as technical write-ups and proof-of-conceptexploits, for which we design novel feature sets. This view also allows us toinvestigate the effect of the label biases on the classifiers. We characterizethe noise-generating process for exploit prediction, showing that our problemis subject to the most challenging type of label noise, and propose techniquesto learn EE in the presence of noise. On a dataset of 103,137 vulnerabilities,we show that EE increases precision from 49% to 86% over existing metrics,including two state-of-the-art exploit classifiers, while its precisionsubstantially improves over time. We also highlight the practical utility of EEfor predicting imminent exploits and prioritizing critical vulnerabilities. Wedevelop EE into an online platform which is publicly available athttps://exploitability.app/.

Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits