Researchers have developed a defensive boosting approach for online probabilistic forecasting, enabling more robust predictions against adaptive adversaries. This method leverages a weak hypothesis class to achieve two key guarantees: competing with the best predictor induced by the span of the hypothesis class on every sequence, and providing a strong guarantee on the Brier score. By combining these guarantees, the approach enhances the resilience of online learning algorithms against sophisticated threats. The technique is particularly relevant in scenarios where state-aligned threat activity is involved, as it raises the stakes from criminal to geopolitical implications. The study focuses on binary outcomes chosen by an adaptive adversary, making it applicable to high-stakes forecasting scenarios. This development matters to practitioners because it offers a more effective way to counter adaptive threats, allowing them to better anticipate and prepare for potential attacks1.