In this work, we present an efficient secure multi-party computation MPC protocol that provides strong security guarantees in settings where potentially a majority of the participants may be malicious and behave arbitrarily. Our protocol achieves both complete identifiability and robustness. With complete identifiability, honest parties can detect and unanimously agree on the identity of any malicious party. Robustness allows the protocol to continue with the computation without requiring a restart, even when malicious behavior is detected. Additionally, our approach addresses the performance limitations observed in MPC protocols which also achieve strong security properties. Finally, we benchmark our protocol on a ML-as-a-service scenario, wherein clients off-load the desired computation to the servers, and verify the computation result. Our benchmarking focuses on linear ML inference, running on various datasets.