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SCA: security update for vllm (GHSA-5jv2-g5wq-cmr4)

medium Tenable Cloud Security Plugin ID 444834

Description

There are packages installed that are affected by a vulnerability referenced in the following CVE:

 - 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. (CVE-2026-53923)

Solution

Update the vllm library and its related packages to version 0.24.0 or later.

See Also

https://github.com/advisories/GHSA-5jv2-g5wq-cmr4

Plugin Details

Severity: Medium

ID: 444834

Version: Revision 1.1

Type: Local

Family: SCA Checks

Published: 7/17/2026

Updated: 7/17/2026

Risk Information

VPR

Risk Factor: Low

Score: 3

Percentile: 23.72

Vendor

Vendor Severity: Medium

CVSS v2

Risk Factor: High

Base Score: 7.8

Temporal Score: 5.8

Vector: CVSS2#AV:N/AC:L/Au:N/C:C/I:N/A:N

CVSS Score Source: CVE-2026-53923

CVSS v3

Risk Factor: High

Base Score: 7.5

Temporal Score: 6.5

Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Temporal Vector: CVSS:3.0/E:U/RL:O/RC:C

CVSS v4

Risk Factor: Medium

Base Score: 5.3

Threat Score: 1.3

Threat Vector: CVSS:4.0/E:U

Vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N

Vulnerability Information

Exploit Ease: No known exploits are available

Patch Publication Date: 6/17/2026

Vulnerability Publication Date: 6/17/2026

Reference Information

CVE: CVE-2026-53923