CVE-2026-53923vLLM is an inference and serving engine for large language models (LLMs).
Updated under an hour ago ()
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No exploitation reported and no EPSS estimate yet.
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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
- 7.5 HIGH (v3.1, NVD)CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
- Weakness (CWE)
- CWE-200, CWE-681
- Known exploited
- Not in CISA's KEV catalog
- EPSS (exploitation in the next 30 days)
- Not loaded for this CVE
- Assigned by
- Not given
- NVD analysis
- Analyzed
- Record last updated
- Jun 24, 2026
- First seen by InfoSec Signals
- Oct 9, 2026
Affected products
vllm vllm vllm:vllm | from 0.5.5 before 0.23.1 | NVD |
References
- https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225epatch
- https://github.com/vllm-project/vllm/pull/44971issue-tracking
- https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4third-party-advisory
The CVE record on cve.org NVD's entry
The CVE record is authoritative. Products marked “from the name” were identified from the CNA's free-text names, not a CPE, and are matched less certainly. This product uses the NVD API but is not endorsed or certified by the NVD.