Skip to main content
VulnerabilityPublished

CVE-2026-53923: vllm · Incorrect Conversion Between Numeric Types

Affects vllmSource CVE Database V5

0
Compare

Not known to be exploited · CVSS 5.3 Medium : upgrade to 0.23.1rc0 when convenient.

Data as of 5 Nov 2026, 23:24 UTC

CVSS 4.0 base score
5.3Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 41% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.23.1rc0
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

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 22 Jun 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 5 Nov 2026.

What to do

Upgrade vllm to 0.23.1rc0.

Affected software

ProductAffectedFixed in
vllm-projectvllmAffected
  • >=0.5.5 <0.23.1rc0
Fixed in
  • 0.23.1rc0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

A fix is available in vLLM version 0.23.1rc0. Users should upgrade to this version or later to remediate the vulnerability. Until upgraded, users should consider the risk of information disclosure in multi-tenant environments.

Description

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.

References

Community reviews

Loading reviews…