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CVE-2026-115919

Description

A flaw was found in ONNX. This vulnerability allows an attacker to cause a Denial of Service (DoS) by providing a specially crafted machine learning model with malformed neural network nodes. During the shape inference process, the application attempts to read memory outside the intended boundaries without validating input dimensions, leading to an application crash.

Statement

Red Hat has evaluated this vulnerability as having a Moderate severity impact because exploitation requires an application or pipeline to process an untrusted, specially crafted machine learning model, and the outcome is limited to a process termination (denial of service). In environments such as Red Hat OpenShift AI workbench and pipeline runtimes, models are typically supplied by authenticated users or data scientists within isolated container boundaries. While parsing an invalid model triggers an application crash during shape inference, the flaw does not facilitate remote code execution, privilege escalation, or unauthorized data exposure.

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 HatNVDcve.org
Base Score6.2N/A6.2
Attack VectorLocalN/ALocal
Attack ComplexityLowN/ALow
Privileges RequiredNoneN/ANone
User InteractionNoneN/ANone
ScopeUnchangedN/AUnchanged
ConfidentialityNoneN/ANone
Integrity ImpactNoneN/ANone
Availability ImpactHighN/AHigh

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

Frequently Asked Questions

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