CVE-2026-115920
Description
A flaw was found in ONNX. When processing a crafted machine learning model containing a transpose convolution (ConvTranspose) operator with an undersized weight tensor, an out-of-bounds read occurs during dimension calculation. An attacker can exploit this issue by supplying a malicious model to an application performing shape inference, causing the application to crash and resulting in a denial of service (DoS).
Statement
Red Hat has evaluated this vulnerability as Moderate because exploitation is limited to a local denial-of-service condition without integrity or confidentiality impact. In Red Hat OpenShift AI environments, triggering the defect requires an application or pipeline to load and run shape inference on an untrusted, specially structured machine learning model. Because model ingestion typically happens within isolated workbench or pipeline runtime containers, any resulting crash is confined to that specific workload process.
Common Vulnerability Scoring System (CVSS) Score Details
Info alert:Important note
CVSS scores for open source components depend on vendor-specific factors (e.g. version or build chain). Therefore, Red Hat's score and impact rating can be different from NVD and other vendors. Red Hat remains the authoritative CVE Naming Authority (CNA) source for its products and services (see Red Hat classifications).
The following CVSS metrics and score provided are preliminary and subject to review.
CVSS v3 Score Breakdown
| Red Hat | NVD | cve.org | |
|---|---|---|---|
| Base Score | 6.2 | N/A | 6.2 |
| Attack Vector | Local | N/A | Local |
| Attack Complexity | Low | N/A | Low |
| Privileges Required | None | N/A | None |
| User Interaction | None | N/A | None |
| Scope | Unchanged | N/A | Unchanged |
| Confidentiality | None | N/A | None |
| Integrity Impact | None | N/A | None |
| Availability Impact | High | N/A | High |
Vector
Red Hat: CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
cve.org: CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
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