LATIDIA · Ciberseguridad

Activación de puertas traseras de mapas de funciones sigilosas a través de la inyección de fallas físicas en redes neuronales integradas

arXiv: 2607.09473v2Tipo de anuncio: reemplazar Resumen: Los ataques de inyección de fallas (FI) en las implementaciones de redes neuronales embebidas (NN) se centran principalmente en inducir clasificaciones erróneas mediante ponderaciones corruptas o cálculos intermedios

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arXiv:2607.09473v2 Announce Type: replace Abstract: Fault injection (FI) attacks on embedded neural network (NN) implementations primarily focus on inducing misclassification by corrupting weights or intermediate computations, overlooking their interaction with algorithmic adversarial threats. In this work, we present a cross-level attack that bridges implementation-level physical faults to algorithm-level adversarial attacks. By characterizing fault-induced data perturbations during NN inference, we connect FI with backdoor learning, enabling system-level attacks that jointly exploit implementation- and algorithm-level vulnerabilities. Specifically, we propose a precise fault-injection method that reliably manipulates targeted register values to tractable states during execution. Leveraging this level of FI precision, we propose a novel end-to-end feature map-level backdoor attack, where physically induced