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
| Vendor | Product | Affected | Fixed |
|---|---|---|---|
| vllm-project | vllm | >= 0.5.5, < 0.23.1rc0 | — |
Affected packages
| Ecosystem | Package | Affected | Fixed | Record |
|---|---|---|---|---|
| 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
- WID-SEC-2026-1897 vllm: Mehrere Schwachstellen BSI CERT-Bund (WID) · Oct 5, 2026