Hardware reverse engineering has traditionally been a labor-intensive and prohibitively expensive component of embedded system security analysis, primarily due to the need for expert manual physical inspection. This presentation introduces RASPUTIN (Robotic Analysis System for Physically Uncovering the Technology INside), an extensible, automated framework designed to transform the economics and scalability of hardware security analysis.
RASPUTIN combines a high-precision 3+1 axis CNC platform, automated optical imaging, machine learning-based component recognition, and integrated probing to enable end-to-end automation of printed circuit board assembly (PCBA) analysis. Built using commercial off-the-shelf (COTS) components, the system features a modular architecture—including pneumatic tool-changing capabilities—that supports future enhancements.
Funded by DARPA, the project resulted in the successful automated extraction of data from SOIC-8 flash memory chips, demonstrating the feasibility of automating tasks once reliant on expert intervention. RASPUTIN is now in early trials with Alpha-level customers. By dramatically reducing the cost and time required for hardware analysis, RASPUTIN empowers defenders to keep pace with the rapid introduction of new embedded devices, addressing a critical bottleneck in contemporary cybersecurity workflows.