CVE-2026-53923

Summary

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 Software

Weaknesses

• CWE-681: CWE-681: Incorrect Conversion between Numeric Types
• CWE-200: CWE-200: Exposure of Sensitive Information to an Unauthorized Actor

ADP Enrichment

CISA ADP Vulnrichment

• SSVC:
• Exploitation: none
  • Automatable: no
  • Technical Impact: partial

References

• https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4
• https://github.com/vllm-project/vllm/pull/44971
• https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e

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