vLLM up to 0.5.4 numeric conversion

Summaryinfo

A vulnerability categorized as critical has been discovered in vLLM up to 0.5.4. This affects an unknown part. Executing a manipulation can lead to numeric conversion. The identification of this vulnerability is CVE-2026-53923. The attack may be launched remotely. There is no exploit available. It is advisable to upgrade the affected component.

Detailsinfo

A vulnerability was found in vLLM up to 0.5.4. It has been declared as critical. This vulnerability affects an unknown functionality. The manipulation with an unknown input leads to a numeric conversion vulnerability. The CWE definition for the vulnerability is CWE-681. When 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. As an impact it is known to affect confidentiality, integrity, and availability. CVE summarizes:

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.

The advisory is available at github.com. This vulnerability was named CVE-2026-53923 since 06/11/2026. The exploitation appears to be easy. The attack can be initiated remotely. No form of authentication is required for a successful exploitation. Successful exploitation requires user interaction by the victim. The technical details are unknown and an exploit is not available.

Upgrading to version 0.23.1rc0 eliminates this vulnerability. Applying the patch f219788f91952827132fa4fdf916427cd20d225e is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.

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Productinfo

Name

• vLLM

Version

• 0.5.0
• 0.5.1
• 0.5.2
• 0.5.3
• 0.5.4

License

• open-source

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