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  <updated>2026-10-06T16:15:53.824386+00:00</updated>
  <author>
    <name>Vulnerability-Lookup</name>
    <email>csirt@opendfir.org</email>
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  <entry>
    <id>https://cve.radiocsirt.org/vuln/cve-2026-53923</id>
    <title>CVE-2026-53923 — vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow</title>
    <updated>2026-10-06T16:15:53.843275+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> vllm-project vllm</p>
<p>vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.</p></div>
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    <link href="https://cve.radiocsirt.org/vuln/cve-2026-53923"/>
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