QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
Codepurify: Defend backdoor attacks on neural code models via entropy-based purification,
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Natural backdoors are prevalent in CodeLMs; the authors propose ScanNBT to detect them after analyzing differences from injected backdoors, transferability, and causes.
citing papers explorer
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Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors
QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
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Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
Natural backdoors are prevalent in CodeLMs; the authors propose ScanNBT to detect them after analyzing differences from injected backdoors, transferability, and causes.