CVE-2026-53923

HIGHNVD 7.57.5—
EchelonGraph scoreMEDIUM confidence

This high-severity CVE scores 7.5 under NVD CVSS v3. EPSS exploit probability: 0.5%, top 61% of all CVEs by exploit prediction. GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).

Triggered by: NVD CVSS baseline
Sources: epss, nvd
7.5EG
EchelonGraph verdictPlan a fixSerious severity, but no confirmed exploitation yet.
  • High severity, but no confirmed exploitation yet
CISA-KEV: Not listedEPSS PROB: 0%CVSS: 7.5Exploit: None knownExposed: 0

No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.

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.

CVSS v3
7.5
EG Score
7.5(medium)
EG Risk
34(Track)
EG Risk 34/100SSVC: Track

EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).

How it’s computed
Severity75% × 45%
Exploitation0% × 40%
Automatability0% × 15%
Action: Routine — remediate on your standard cadence.
EPSS PROB
0%
EPSS %ILE
40%
KEV
Not listed

Published

June 17, 2026

Last Modified

June 24, 2026

Advisory Details (3)

Auto-updated Jun 23, 2026
Patch available. Sources: github_pr, github, github_commit.
github Patch Available

GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving · Advisory · vllm-project/vllm · GitHub

https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4
github_pr

[Security] Fix info disclosure via int32 truncation in GGUF dequantize kernels

Fix merged in vllm-project/vllm PR #44971 on 2026-06-11 — awaiting tagged release

https://github.com/vllm-project/vllm/pull/44971
github_commit

commit f219788f9195 (vllm-project/vllm)

Fix landed in vllm-project/vllm commit f219788f9195 — awaiting tagged release

https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e

Vendor Advisories for CVE-2026-53923(1)

These vendors published their own advisory mentioning this CVE — often with vendor-specific remediation steps + affected product lists not in NVD.

Patch Availability(1)

Vendor / EcosystemFixed in / PatchReleasedSource
pipvllm—ghsa

Patches are aggregated from vendor advisories (Red Hat, Microsoft, Cisco, GitHub) and package ecosystems (OSV, GHSA). Multiple rows for the same upstream release have been deduplicated.

Affected Packages

(1 across 1 ecosystem)
PyPI(1)
PackageVulnerable rangeFixed inDependents
vllm0.10.0 ... 0.9.2 (55 versions)0.24.0—

Weakness Classification(2)

MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.

Data Freshness Timeline

(refreshed 10× in last 7d / 50× in last 30d)

Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.

Showing the most recent 100 of 155 total refreshes for this CVE.

  1. 2026-08-25 13:49 UTCEPSS rescore
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  1. 2026-08-09 20:16 UTCEG score recompute
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Related CVEs(same product + same CWE)

Frequently asked(5)

What is CVE-2026-53923?
CVE-2026-53923 is a high vulnerability published on June 17, 2026. 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…
When was CVE-2026-53923 disclosed?
CVE-2026-53923 was first published in the National Vulnerability Database on June 17, 2026, with the most recent update on June 24, 2026. EchelonGraph re-ingests CVE updates from NVD on a 2-hour cycle, so this page reflects the latest published state.
Is CVE-2026-53923 actively exploited?
CVE-2026-53923 is not currently on CISA's Known Exploited Vulnerabilities catalog. FIRST EPSS estimates a 0% probability of exploitation in the next 30 days, which ranks it in the top 60.5% of all scored CVEs.
What is the CVSS score of CVE-2026-53923?
CVE-2026-53923 has a CVSS v3 base score of 7.5 (NVD).
How do I remediate CVE-2026-53923?
Patch to the fixed version published by the affected vendor. Where vendor advisories exist for CVE-2026-53923, EchelonGraph cross-links them in the Vendor Advisories panel below — those typically contain the canonical remediation steps, fixed version numbers, and any vendor-specific mitigations.

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