Mapping and detecting exploitable voltage glitches using clock traces and neural networks

Voltage fault injection (VFI) is a hardware-level exploitation technique in which a precisely timed disturbance is introduced to a device’s power supply to induce computational faults. These deliberately injected faults can bypass critical security mechanisms or expose sensitive information. Conventional hardware-based countermeasures, including power-filtering circuits and voltage supervisors, often rely on static detection thresholds and can be circumvented by advanced glitching tools capable of generating perturbations too brief for fixed-trip circuits to detect. This paper presents a Convolutional Neural Network (CNN)-based framework for adaptive detection of voltage glitch attacks. Our method builds on the principle that the system clock signal, which governs processor timing, provides a more sensitive and persistent indicator of glitch-induced perturbations than the compromised power rail. We perform a thorough VFI evaluation, characterizing a wide range of glitch parameters against an AES-128 encryption routine executed on an STM32F410 microcontroller. We delineate the exploitable region of the glitch parameter space using a Support Vector Machine (SVM), thereby avoiding reliance on arbitrary estimations to delimitate exploitable behavior. The resulting exploitable parameter ranges are labeled within the dataset as exploitable and safe executions, which are then used to train the CNN. The resulting model is a CNN classifier that accurately detects exploitable glitch propagation, achieving 98.6% accuracy. The results demonstrate that this approach is well suited for adaptive and resilient hardware security primitives and for mitigating next-generation glitch attacks.