AIBearisharXiv – CS AI · 9h ago7/10
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Widening the Gap: Exploiting LLM Quantization via Outlier Injection
Researchers demonstrate the first practical quantization-conditioned attack that reliably compromises large language models across advanced quantization methods including AWQ, GPTQ, and GGUF. The attack exploits how outlier weights cause rounding errors in modern quantization schemes, allowing adversaries to inject hidden malicious behaviors that activate only after quantization, posing significant security risks to the deployment pipeline.