The number of software vulnerabilities discovered and publicly disclosed is increasing every year; however, only a small fraction of these vulnerabilities are exploited in real-world attacks. With limitations on time and skilled resources, organizations often look at ways to identify threatened vulnerabilities for patch prioritization. In this chapter, an exploit prediction model is presented, which predicts whether a vulnerability will likely be exploited. Our proposed model leverages data from a variety of online data sources (white hat community, vulnerability research community, and dark web/deep web (DW) websites) with vulnerability mentions. Compared to the standard scoring system (CVSS base score) and a benchmark model that leverages Twitter data in exploit prediction, our model outperforms the baseline models with an F1 measure of 0.40 on the minority class (266% improvement over CVSS base score) and also achieves high true positive rate and low false positive rate (90%, 13%, respectively), making it highly effective as an early predictor of exploits that could appear in the wild. A qualitative and a quantitative study are also conducted to investigate whether the likelihood of exploitation increases if a vulnerability is mentioned in each of the examined data sources. The proposed model is proven to be much more robust than adversarial examples—postings authored by adversaries in the attempt to induce the model to produce incorrect predictions. A discussion on the viability of the model is provided, showing cases where the classifier achieves high performance, and other cases where the classifier performs less efficiently.