2 papers
cs.CR2026
FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks
Aoying Zheng, Anqi Du, Zizhuang Deng +1
Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Conditioned Backdoor (QCB) attac…
cs.CR2026
QuantGuard: Learnable Rounding for Repairing Quantization-Conditioned Backdoors in LLMs
Aoying Zheng, Anqi Du, Zizhuang Deng +3
Model quantization is a key technique for reducing storage and inference costs in large language model deployment. However, recent studies show that the discretization and rounding…