<ul><li>Post-training quantization is standard for memory-efficient deployment of large language models.</li><li>Recent work has shown that basic rounding-based quantization schemes like GGUF pose security risks.</li><li>An attack has been introduced on GGUF quantization, exploiting quantization errors to construct malicious models.</li><li>The attack demonstrated effectiveness on three popular large language models across various scenarios.</li></ul>

Mind the Gap: A Practical Attack on GGUF Quantization

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