This letter proposed a hybrid credibility learning method for data-driven Dynamic Security Assessment (DSA), aiming to enhance the trustworthiness of DSA results under out-of-distribution changing system conditions. The proposed method integrates multiple credibility criteria as learning inputs, combining model consistency-reflected through the output distribution of ensemble learners-with novel indicators of data disparities, namely the Degree of Outlier (DOO) and One-sample Maximum Mean Discrepancy (O-MMD). Moreover, the credibility model benefits from training on a specifically designed out-of-distribution dataset which is further reinforced through misclassified DSA instances. This letter also provides a comprehensive comparison of existing credible DSA methods via the proposed trustworthiness evaluation index. Simulation results on the benchmark testing system show that the proposed method can achieve the best trade-off between DSA accuracy and credibility under various extreme system conditions.