LATIDIA · Ciberseguridad

RAMPA: revertir las perturbaciones adversas para fortalecer los ataques de puerta trasera de etiqueta limpia contra los detectores de malware

arXiv: 2609.27422v1Tipo de anuncio: nuevo Resumen: Los detectores de malware basados en el aprendizaje profundo se actualizan comúnmente mediante el ajuste fino de las muestras recién recolectadas, pero esta actualización práctica también crea una superficie de ataque para

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arXiv:2609.27422v1 Announce Type: new Abstract: Deep learning-based malware detectors are commonly updated by fine-tuning on newly collected samples, but this practical update pipeline also creates an attack surface for training-time backdoor attacks. In realistic crowdsourced data collection, however, strict label vetting typically restricts attackers to the clean-label setting, in which poisoned samples must retain benign labels and functionality, making effective backdoor injection substantially harder. We present a new attack perspective based on feature-space manipulation: instead of relying solely on stronger trigger designs or selecting benign samples that are naturally similar to malware, we deliberately construct benign programs whose representations shift toward the malware region before trigger injection, thereby creating stronger