CVE-2026-34445
Published: 01 April 2026
Summary
CVE-2026-34445 is a high-severity Improper Input Validation (CWE-20) vulnerability in Linuxfoundation Onnx. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked at the 42.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Machine Learning Libraries.
The strongest mitigations our analysis identified are NIST 800-53 SI-10 (Information Input Validation) and SI-2 (Flaw Remediation).
Threat & Defense at a Glance
Threat & Defense Details
Mitigating Controls (NIST 800-53 r5)AI
Directly addresses the improper input validation in ExternalDataInfo by requiring validation of metadata keys from ONNX model files before applying setattr().
Ensures timely patching and upgrade to ONNX version 1.21.0 to remediate the specific flaw allowing object property overwrites.
Vulnerability scanning identifies systems using vulnerable ONNX versions prior to 1.21.0 for remediation.
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability allows remote exploitation via a malicious ONNX model file processed by client applications using the ONNX library, enabling client-side code execution (T1203) and application denial of service through resource exhaustion (T1499.004), as indicated by high availability impact and CWE-400.
NVD Description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python’s setattr() function to load metadata (like file paths or data lengths) directly from an ONNX…
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model file. It didn’t check if the "keys" in the file were valid. Due to this, an attacker could craft a malicious model that overwrites internal object properties. This issue has been patched in version 1.21.0.
Deeper analysisAI
CVE-2026-34445 is a vulnerability in the Open Neural Network Exchange (ONNX) library, an open standard for machine learning model interoperability, affecting versions prior to 1.21.0. The issue lies in the ExternalDataInfo class, which uses Python's setattr() function to load metadata—such as file paths or data lengths—directly from an ONNX model file without validating the keys present in the file. This lack of validation enables an attacker to craft a malicious ONNX model that overwrites internal object properties of the class.
The vulnerability can be exploited remotely by an unauthenticated attacker with no privileges required and no user interaction needed, as indicated by its CVSS 3.1 score of 8.6 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H). Exploitation occurs when a victim system loads or processes the malicious ONNX model file, potentially leading to low confidentiality impact, low integrity impact, and high availability impact. The associated weakness identifiers are CWE-20 (Improper Input Validation), CWE-400 (Uncontrolled Resource Consumption), and CWE-915 (Improperly Controlled Modification of Dynamically-Determined Object Attributes).
Mitigation is available through an upgrade to ONNX version 1.21.0, where the issue has been patched. Official resources detail the fix, including the patch commit at https://github.com/onnx/onnx/commit/e30c6935d67cc3eca2fa284e37248e7c0036c46b, the corresponding pull request at https://github.com/onnx/onnx/pull/7751, and the GitHub security advisory at https://github.com/onnx/onnx/security/advisories/GHSA-538c-55jv-c5g9.
Details
- CWE(s)
Affected Products
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: neural network, machine learning