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cs.CR2026
Beyond Vector Hiding: Breaking and Mitigating Shared-Direction Weight Obfuscation in TEE-Offloaded Large Language Models
Menghui Zhang, Aoying Zheng, Guoxiao Liu +4
Trusted Execution Environment (TEE)-shielded partitioning of Large Language Models (LLMs) accelerates on-device inference by offloading obfuscated linear layers to an untrusted acc…
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…