CVE-2026-53923: vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow

Vulnerability summary

CVE-2026-53923 is a high (CVSS 7.5) vulnerability in vllm-project vllm. EPSS estimates a 0.48% chance of exploitation in the next 30 days, higher than 40.7% of scored vulnerabilities.

Timeline

1. 22-06-2026
   PublishedVulnerability first disclosed.
2. 23-06-2026
   Last modifiedVulnerability information updated.

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.

• CWE-200Exposure of Sensitive Information to an Unauthorized ActorThe product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.
• CWE-681Incorrect Conversion between Numeric TypesWhen converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.