Information Exposure Affecting vllm-wheels package, versions *
Threat Intelligence
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-MINIMOSLATEST-VLLMWHEELS-17376886
- published19 Jun 2026
- disclosed22 Jun 2026
Introduced: 19 Jun 2026
NewCVE-2026-53923 (opens in a new tab)How to fix?
There is no fixed version for Minimos:latest vllm-wheels.
NVD Description
Note: Versions mentioned in the description apply only to the upstream vllm-wheels package and not the vllm-wheels package as distributed by Minimos.
See How to fix? for Minimos:latest relevant fixed versions and status.
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
- https://www.cve.org/CVERecord?id=CVE-2026-53923
- https://github.com/advisories/GHSA-5jv2-g5wq-cmr4
- https://osv.dev/vulnerability/MINI-qv8g-w4fh-gcw6
- https://osv.dev/vulnerability/CVE-2026-53923
- https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e
- https://github.com/vllm-project/vllm/pull/44971
- https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4