CVE-2026-53923
vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow
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.
INFO
Published Date :
June 22, 2026, 11:16 p.m.
Last Modified :
June 24, 2026, 4:51 p.m.
Remotely Exploit :
Yes !
Source :
[email protected]
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS | 134c704f-9b21-4f2e-91b3-4a467353bcc0 | |||||
| CVSS 3.1 | HIGH | [email protected] | ||||
| CVSS 4.0 | MEDIUM | [email protected] |
Solution
- Update vLLM to 0.23.1rc0 or later.
- Review tensor processing for truncation vulnerabilities.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-53923.
| URL | Resource |
|---|---|
| https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e | Patch |
| https://github.com/vllm-project/vllm/pull/44971 | Issue Tracking |
| https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4 | Third Party Advisory |
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-53923 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-53923
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-53923 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-53923 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
Initial Analysis by [email protected]
Jun. 24, 2026
Action Type Old Value New Value Added CVSS V3.1 AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N Added CPE Configuration OR *cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* versions from (including) 0.5.5 up to (excluding) 0.23.1 Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e Types: Patch Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/pull/44971 Types: Issue Tracking Added Reference Type GitHub, Inc.: https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4 Types: Third Party Advisory -
CVE Modified by 134c704f-9b21-4f2e-91b3-4a467353bcc0
Jun. 23, 2026
Action Type Old Value New Value Added SSVC {'id': 'CVE-2026-53923', 'role': 'CISA Coordinator', 'options': [{'exploitation': 'none'}, {'automatable': 'no'}, {'technicalImpact': 'partial'}], 'version': '2.0.3', 'timestamp': '2026-06-23T15:04:15.555317Z'} -
New CVE Received by [email protected]
Jun. 22, 2026
Action Type Old Value New Value Added Affected [{'vendor': 'vllm-project', 'product': 'vllm', 'versions': [{'status': 'affected', 'version': '>= 0.5.5, < 0.23.1rc0'}]}] Added 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. Added CVSS V4.0 AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X Added CWE CWE-200 Added CWE CWE-681 Added Reference https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e Added Reference https://github.com/vllm-project/vllm/pull/44971 Added Reference https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4