5.3
Medium
CVE-2026-53923 vLLM is an inference and serving engine for large language models (LLMs)
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.
Affected products and versions
| Product | Affected versions | Fixed in |
|---|---|---|
| vllm 0.5 | 0.5.5 → < 0.23.1rc0 | 0.23.1rc0 |
Other CVEs of vllm
The 121 CVEs of vllm →
CVE-2026-115918
vLLM is an inference and serving engine for large language models
6.5
Medium
CVE-2026-115917
vLLM is an inference and serving engine for large language models
3.4
Low
CVE-2026-115916
vLLM is an inference and serving engine for large language models
6.5
Medium
CVE-2026-115915
vLLM is an inference and serving engine for large language models
8.1
High