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Advisory details: vulnrichment / CVE-2026-53923
Advisory ID vulnrichment/CVE-2026-53923
Aliases
Summary 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.
Status Published
Exploitability 0.5
Weighted Severity 0.0
Risk
Affected and Fixed Packages Package Details
Introduced and Fixed Package Commit Patches Package Commit Patches Details
Weaknesses (2)
System Score Found at
cvssv4 5.3
ssvc Track
No exploits are available.

No EPSS data available for this advisory.

Patch URL
There are no known patches.
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 Found at None
Attack Vector (AV) Attack Complexity (AC) Attack Requirements (AT) Privileges Required (PR) User Interaction (UI) Vulnerable System Impact Confidentiality (VC) Vulnerable System Impact Integrity (VI) Vulnerable System Impact Availability (VA) Subsequent System Impact Confidentiality (SC) Subsequent System Impact Integrity (SI) Subsequent System Impact Availability (SA)

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Vector: SSVCv2/E:N/A:N/T:P/P:M/B:A/M:M/D:T/2026-06-23T15:04:15Z/ Found at None

Vector: SSVCv2/E:N/A:N/T:P/P:M/B:A/M:M/D:T/2026-06-23T15:04:15Z/

Decision: Track

Source URL: https://github.com/cisagov/vulnrichment/blob/develop/2026/53xxx/CVE-2026-53923.json

Source Advisory: vulnrichment/CVE-2026-53923

View SSVC decision tree
[{'Exploitation': 'none'},
 {'Automatable': 'no'},
 {'Technical Impact': 'partial'},
 {'Mission Prevalence': 'minimal'},
 {'Public Well-being Impact': 'material'},
 {'Mission & Well-being': 'medium'}]