LATIDIA · Ciberseguridad

MOMAT: Mezcla de múltiples atlas para la defensa de jailbreak de baja potencia de LLM cuantificados

arXiv: 2610.01058v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes cuantificados se implementan cada vez más en dispositivos de borde por su baja latencia y eficiencia energética. Sin embargo, la cuantificación del modelo debilita la alineación de safegua

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arXiv:2610.01058v1 Announce Type: new Abstract: Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, leaving qLLMs (quantized large language models) highly vulnerable to jailbreak attacks. To address this challenge, we present MOMAT (Mixture of Multiple Atlases), a hardware-enhanced safety framework that combines structured knowledge retrieval with low-power defense acceleration. Each atlas represents a semantic cluster of harmful or benign sample sets and policy templates, enabling domain-localized Retrieval-Augmented Generation guarding that mitigates the curse of dimensionality and the resulting semantic sparsity problem in large, heterogeneous safety databases. MOMAT retrieves top-$k$ similarity features from all atlases for each