CVE-2026-53923: vLLM is an inference and serving engine for large language models (LLMs)
CVE-2026-53923 is a high-severity flaw (CVSS 7.5) affecting vllm. Below: why it ranks where it does, which versions are affected, what to do, and the exact commands.
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 te
Why this priority
- High severity
- Remotely exploitable, no login, no user action
A fixed release or distro patch exists.
Last updated 2026-10-25 · Sources: CVE Program, NVD, CISA KEV, EPSS and vendor advisories · How we score
Am I affected?
Timeline
- Published CVE record published
- Updated Record last modified
Scores and fixes here are computed automatically from those and other public sources. Confirm with the vendor advisory before changing production systems.
Quick answers
How do I fix CVE-2026-53923?
vllm: upgrade to 0.23.1. Exact commands for each OS are on this page.
Is CVE-2026-53923 being exploited?
CVE-2026-53923 is not on the CISA Known Exploited Vulnerabilities list at the time of the last update. Check again, as this can change.
What does CVE-2026-53923 affect?
CVE-2026-53923 affects vllm. Severity is high (CVSS 7.5). 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 pr
How urgent is CVE-2026-53923?
Priority 42/100. High severity; Remotely exploitable, no login, no user action
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