While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This paper presents SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment) that is a generic and explainable framework that propagates uncertainties via sensitivities for real-time risk assessment in VIO. The scientific novelty lies in the derivation of a real-time risk indicator that is backend-agnostic and exploits the Schur complement blocks of the Gauss-Newton normal matrix to propagate uncertainties. Practically, the Schur complement captures the sensitivity that reflects the influence of the uncertainty on the risk occurrence. Our framework estimates risks on the basis of the residual magnitudes, geometric conditioning, and short horizon temporal trends without requiring ground truth knowledge. Our framework enables to reliably predict trajectory degradation 50 frames ahead with an improvement of 20% to the baseline. In addition, SUPER initiates a stop or relocalization policy with 89.1% recall. The framework is backend agnostic and operates in real time with less than 0.2% additional CPU cost. Experiments show that SUPER provides consistent uncertainty estimates. A SLAM evaluation highlights the applicability to long horizon mapping.