Information Exposure Affecting vllm-cu130 package, versions <0.24.0-r0


Severity

Recommended
low

Based on default assessment until relevant scores are available.

Threat Intelligence

EPSS
0.28% (20th percentile)

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  • Snyk IDSNYK-MINIMOSLATEST-VLLMCU130-17741077
  • published1 Jul 2026
  • disclosed22 Jun 2026

Introduced: 22 Jun 2026

CVE-2026-53923  (opens in a new tab)
CWE-200  (opens in a new tab)
CWE-681  (opens in a new tab)

How to fix?

Upgrade Minimos:latest vllm-cu130 to version 0.24.0-r0 or higher.

NVD Description

Note: Versions mentioned in the description apply only to the upstream vllm-cu130 package and not the vllm-cu130 package as distributed by Minimos. See How to fix? for Minimos:latest relevant fixed versions and status.

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.