MOMAT is a low-power defense framework for quantized large language models (qLLMs) against jailbreak attacks. It uses a mixture of multiple atlases to retrieve and evaluate similarity features from a lightweight MoE detector, accelerating retrieval with a CiM-accelerated similarity engine. MOMAT achieves a 4.69 million times speedup and 2.5 million times energy reduction over DRAM-based baselines, making edge-deployed qLLMs safer and more energy-efficient.