We develop the SNPeek framework to assist developers evaluate side-channel assisted privacy attacks that are broadly applicable to Confidential virtual machines (CVMs). The privacy reduction due to these attacks heavily depend on the execution environment and the workload, which varies vastly: What are available attack primitives? How does the particular privacy workload behave? This makes manual investigation and efficiently mitigating software-based side channels a cumbersome task. SNPeek solves this challenge by providing a set of configurable attack primitives that can execute on real CVM hardware, as well as automated ML-based analysis pipelines. We evaluate the effectiveness of SNPeek on privacy-preserving workloads. Our results show that our approach is effective at pinpointing the vulnerability of privacy apps against side channels, and can help evaluate mitigations based on oblivious memory and differential privacy.