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Rotated Robustness: A Training-Free Defense against Bit-Flip Attacks on Large Language Models
Bit-flip corruption of quantized weights poses a serious reliability threat to Large Language Models (LLMs), as even a small number of weight faults can trigger catastrophic model degradation. We show that such failures can be strongly amplified when corrupted weights are coupled to high-sensitivity
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