CVE-2026-53923
vLLM: GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving
Impact
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 packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| pip | vllm | >= 0.5.5, < 0.24.0 | 0.24.0 |
Affected products
| Vendor | Product | Affected versions |
|---|---|---|
| Vllm | Vllm | from 0.5.5 before 0.23.1 |
Weakness classification
References
- NVD record
- CVE.org record
- GitHub advisory
- https://github.com/advisories/GHSA-5jv2-g5wq-cmr4
- 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
- https://nvd.nist.gov/vuln/detail/CVE-2026-53923
- https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-3403.yaml
- https://github.com/vllm-project/vllm
- https://pypi.org/project/vllm