The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configurations. Fault injection attacks can result in denial of service, disrupting functionality or leaking secret information. This threat is further amplified in multi-tenancy scenarios. Detecting such threats before loading onto the FPGA is crucial, but existing methods face difficulty identifying sophisticated attacks. We present MaliGNNoma, a machine learning-based solution that accurately identifies malicious FPGA configurations. Serving as a netlist scanning mechanism, it can be employed by cloud service providers as an initial security layer within a necessary multi-tiered security system. By leveraging the inherent graph representation of FPGA netlists, MaliGNNoma employs a graph neural network (GNN) to learn distinctive malicious features, surpassing current approaches. To enhance transparency, MaliGNNoma utilizes a parameterized explainer for the GNN, labeling the FPGA configuration and pinpointing the sub-circuit responsible for the malicious classification. Through extensive experimentation on the ZCU102 board with a Xilinx UltraScale+ FPGA, we validate the effectiveness of MaliGNNoma in detecting malicious configurations, including sophisticated attacks, such as those based on benign modules, like cryptography accelerators. MaliGNNoma achieves a classification accuracy and precision of 98.24% and 97.88%, respectively, surpassing state-of-the-art. We compare MaliGNNoma with five state-of-the-art scanning methods, revealing that not all attack vectors detected by MaliGNNoma are recognized by existing solutions, further emphasizing its effectiveness. Additionally, we make MaliGNNoma and its associated dataset publicly available.