Several hyperscalers have recently disclosed the occurrence of Silent Data Corruptions (SDCs) in their systems fleets, sparking concerns about the severity of known and the existence of unidentified root causes of faults in CPUs. These incidents reveal that CPU chips have the potential to generate incorrect results for different tasks due to latent manufacturing defects, variability, marginalities, bugs, and aging. To tackle this problem, we present Harpocrates, an automated methodology for the generation of short, constrained-random functional test programs that maximize fault detection in target CPU structures and can be employed at different stages of system lifetime. Harpocrates stands out by adopting a hardware-model-in-the-loop approach, which iteratively refines the generated test programs using a detailed simulation-based microarchitecture engine. The engine models and grades for multiple hardware fault types that can lead to data corruptions during system operation. Harpocrates is versatile and can adapt to various program generators, ISAs, microarchitectures, and fault types. Our results on six important CPU hardware structures show that Harpocrates attains much shorter test generation times than hardware-agnostic publicly available frameworks and outperforms open-source test suites in terms of fault detection capability.