| Advisory ID | vulnrichment/CVE-2026-53923 |
| Aliases | |
| 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. |
| Status | Published |
| Exploitability | 0.5 |
| Weighted Severity | 0.0 |
| Risk | |
| Affected and Fixed Packages | Package Details |
| Introduced and Fixed Package Commit Patches | Package Commit Patches Details |
| System | Score | Found at |
|---|---|---|
| cvssv4 | 5.3 | |
| ssvc | Track |
No EPSS data available for this advisory.
| Patch URL | |
|---|---|
| There are no known patches. | |
| Attack Vector (AV) | Attack Complexity (AC) | Attack Requirements (AT) | Privileges Required (PR) | User Interaction (UI) | Vulnerable System Impact Confidentiality (VC) | Vulnerable System Impact Integrity (VI) | Vulnerable System Impact Availability (VA) | Subsequent System Impact Confidentiality (SC) | Subsequent System Impact Integrity (SI) | Subsequent System Impact Availability (SA) |
|---|---|---|---|---|---|---|---|---|---|---|
network adjacent local physical |
low high |
none present |
none low high |
none passive active |
high low none |
high low none |
high low none |
high low none |
high low none |
high low none |
Vector: SSVCv2/E:N/A:N/T:P/P:M/B:A/M:M/D:T/2026-06-23T15:04:15Z/
Decision: Track
Source URL: https://github.com/cisagov/vulnrichment/blob/develop/2026/53xxx/CVE-2026-53923.json
Source Advisory: vulnrichment/CVE-2026-53923
[{'Exploitation': 'none'},
{'Automatable': 'no'},
{'Technical Impact': 'partial'},
{'Mission Prevalence': 'minimal'},
{'Public Well-being Impact': 'material'},
{'Mission & Well-being': 'medium'}]