Hadoop is one of the most popular platforms for distributed big data processing, and YARN is at the core of Hadoop 2.0. In the process of data processing, the possibility exists that malicious nodes might compromise the results, sharing incorrect outputs on the public cloud. To address this issue, this paper introduces an efficient YARN security framework, termed CMT-YARN, which incorporates an enhanced convolutional Merkle Tree to ensure the reliability of task execution results. Within a hybrid cloud environment, this framework utilizes the improved CMHT verification technique to conduct dual verification of intermediate and final results, thereby ensuring data integrity and reliability throughout the execution process. Compared to conventional MHT, CMHT reduces additional computational overhead by more than 37% during verification processes. Qualitative and quantitative analyses indicate that with a data sampling rate of 6.8%, CMT-YARN can ensure that the number of malicious acts undetected by the system does not exceed 5; when the sampling rate exceeds 26.9%, the framework can guarantee the detection of all malicious activities. Experimental results on a real Hadoop cluster demonstrate that CMT-YARN significantly enhances computational and storage performance compared to traditional solutions.