Microscaling (MX) formats share one exponent across an element block, creating a new fault surface: a single-bit flip in the shared exponent corrupts all block elements simultaneously. Five MX-compatible 8-bit encodings (MXINT8, MXFP8-E4M3, MXFP8-E5M2, LOG8-SUM, and LOG8-MAX) are compared through exhaustive single-bit weight fault injection across four workloads using gate-level simulation on the CAPRI1 RISC-V SoC, with the primary workload additionally validated on fabricated silicon. Format choice alone can substantially change vulnerability. Three MX-specific mechanisms explain this variation: block-shared exponent amplification, attention routing inversion from exponent-field faults, and log-domain fault filtering in the max-reduction variant. No single format dominates all axes: LOG8-MAX leads in speed and energy but is limited by accuracy on some attention workloads, MXINT8 provides the strongest fault resilience with full accuracy, and LOG8-SUM offers a balanced compromise across speed, accuracy, and resilience. MX element encoding should be treated not only as an accuracy-efficiency choice, but also as a security-relevant design decision.

Side-channel analysis (SCA) is a growing field in hardware security where adversaries extract secret information from embedded devices by measuring physical observables like power consumption and electromagnetic emanation. SCA is a security assessment method used by governmental labs, standardization bodies, and researchers, where testing is not just limited to standardized cryptographic circuits, but it is expanded to AI accelerators, Post Quantum circuits, systems, etc. Despite its importance, SCA is performed on an ad hoc basis in the sense that its flow is not systematically optimized and unified among labs. As a result, the current solutions do not account for fair comparisons between analyses. Furthermore, neglecting the need for interoperability between datasets and SCA metric computation increases students’ barriers to entry. To address this, we introduce SCAPEgoat, a Python-based SCA library1with three key modules devoted to defining file format, capturing interfaces, and metric calculation. The custom file framework organizes side-channel traces using JSON for metadata, offering a hierarchical structure similar to HDF5 commonly applied in SCA, but more flexible and human-readable. The metadata can be queried with regular expressions, a feature unavailable in HDF5. Secondly, we incorporate memory-efficient SCA metric computations, which allow using our functions on resource-restricted machines. This is accomplished by partitioning datasets and leveraging statistics-based optimizations on the metrics. In doing so, SCAPEgoat makes the SCA more accessible to newcomers so that they can learn techniques and conduct experiments faster and with the possibility to expand on in the future.