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Medium · 5.3 CVE-2026-53923 EUVD-2026-38400GHSA-5jv2-g5wq-cmr4PYSEC-2026-3403

vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow

Published Jun 22, 2026 · Updated Jun 23, 2026 · Assigned by GitHub_M

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

VendorProductAffectedFixed
vllm-project vllm >= 0.5.5, < 0.23.1rc0 —

Affected packages

EcosystemPackageAffectedFixedRecord
PyPI vllm >= 0.5.5, < 0.24.0 0.24.0 GHSA-5jv2-g5wq-cmr4
PyPI vllm >= 0.5.5, <= 0.23.0 — PYSEC-2026-3403

Advisories 1

References