Endgame released Ember, an open-source benchmark dataset aimed at advancing research in static malware detection by providing 1.1 million portable executable file sha256 hashes, along with metadata and derived features, without disclosing the actual files to protect intellectual property. This dataset, which includes both training and test samples categorized as malicious, benign, or unlabeled, allows researchers to measure the effectiveness of new machine learning techniques in identifying unseen threats, despite the evolving nature of malware. The Ember benchmark model, a gradient-boosted decision tree trained with LightGBM, achieves a high area under the ROC curve score, though it is not intended for production use. Instead, it serves as a research tool to explore improvements in feature selection, model parameter optimization, and the development of new featureless neural network-based models, encouraging further research in the field and providing a target for studies on countering machine learning-driven attacks.