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• Hardware faults, specifically bit-flips in quantized weights, pose a severe reliability threat to Large Language Models (LLMs), often triggering catastrophic model collapses.technical 90%
• The vulnerability to bit-flip attacks fundamentally stems from the spatial alignment between sensitive weight bits and extreme activation outliers.technical 85%
• Rotated Robustness (RoR) reduces the stochastic collapse rate from 3.15% to 0.00% on Qwen2.5-7B under random bit-flip attacks.result 95%
• RoR sustains robust reasoning on Llama-2-7B under severe targeted attacks with 50 Progressive Bit Search flips, maintaining 43.9% MMLU accuracy that nearly matches its 45.2% unattacked accuracy.result 90%
• Against the Single-Point Fault Attack (SPFA), RoR exponentially inflates the attack complexity from a few bits to over 17,000 precise bit-flips.result 90%
• RoR has a negligible storage overhead of 0.31% and a minimal inference latency increase of 9.1% on Llama-2-7B.technical 95%
• RoR is a training-free defense utilizing orthogonal Householder transformations that applies an orthogonal rotation to the activation space.method 90%
• RoR mathematically guarantees original model accuracy while providing defense against bit-flip attacks.technical 85%

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