CVE-2026-34446 — MEDIUM (CVSS 4.7)

CISO Take

ONNX's onnx.load() bypasses path traversal protections for hardlinks, allowing a malicious model file to read arbitrary files accessible to the loading process. This is a supply chain vector: any team loading ONNX models from untrusted sources is exposed. Patch to 1.21.0 immediately and enforce trusted-source-only model loading policies.

What is the risk?

Medium risk overall, but elevated in AI/ML supply chain contexts. CVSS 4.7 reflects local-only, high-complexity, user-interaction-required conditions. Real-world risk increases significantly when ONNX models are sourced from public repositories or shared storage—common in ML workflows. The high confidentiality impact (API keys, credentials, model weights on the same filesystem) warrants prompt attention despite the medium CVSS.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
onnx	pip	<= 1.20.1	`1.21.0`
20.8K OpenSSF 8.1 1.2K dependents Pushed 7d ago 90% patched ~29d to patch Full package profile →

Do you use onnx? You're affected.

Severity & Risk

CVSS 3.1

4.7 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 3% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Moderate

Attack Surface

AV Local

AC High

PR None

UI Required

S Unchanged

C High

I None

A None

What should I do?

6 steps

Patch: Upgrade onnx to >= 1.21.0 immediately (pip install --upgrade onnx).
Audit: Identify all systems calling onnx.load() against externally-sourced models.
Policy: Enforce cryptographic signing or hash verification of ONNX model files before loading.
Isolation: Run model loading in sandboxed environments with minimal filesystem access (container or VM with restricted mounts).
Detection: Monitor for unexpected file reads during model loading via auditd or eBPF.
Interim workaround if patching is blocked: validate all model sources are fully trusted and restrict filesystem permissions for the loading process user.

CISA SSVC Assessment

Decision Track

Exploitation none

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

Supply Chain Data Extraction Framework Model AML.T0002.001 - Models AML.T0010.001 - AI Software AML.T0011.000 - Unsafe AI Artifacts AML.T0037 - Data from Local System

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.10.1 - Supplier relationships and AI supply chain

NIST AI RMF

GOVERN 6.2 - Policies and procedures for AI risk across the supply chain

OWASP LLM Top 10

LLM03 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2026-34446?

ONNX's onnx.load() bypasses path traversal protections for hardlinks, allowing a malicious model file to read arbitrary files accessible to the loading process. This is a supply chain vector: any team loading ONNX models from untrusted sources is exposed. Patch to 1.21.0 immediately and enforce trusted-source-only model loading policies.

Is CVE-2026-34446 actively exploited?

No confirmed active exploitation of CVE-2026-34446 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-34446?

1. Patch: Upgrade onnx to >= 1.21.0 immediately (pip install --upgrade onnx). 2. Audit: Identify all systems calling onnx.load() against externally-sourced models. 3. Policy: Enforce cryptographic signing or hash verification of ONNX model files before loading. 4. Isolation: Run model loading in sandboxed environments with minimal filesystem access (container or VM with restricted mounts). 5. Detection: Monitor for unexpected file reads during model loading via auditd or eBPF. 6. Interim workaround if patching is blocked: validate all model sources are fully trusted and restrict filesystem permissions for the loading process user.

What systems are affected by CVE-2026-34446?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, MLOps/CI-CD pipelines, model conversion workflows, research/notebook environments.

What is the CVSS score for CVE-2026-34446?

CVE-2026-34446 has a CVSS v3.1 base score of 4.7 (MEDIUM). The EPSS exploitation probability is 0.01%.

Technical Details

NVD Description

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, there is an issue in onnx.load, the code checks for symlinks to prevent path traversal, but completely misses hardlinks because a hardlink looks exactly like a regular file on the filesystem. This issue has been patched in version 1.21.0.

Exploitation Scenario

An adversary creates a malicious ONNX model file containing hardlinks pointing to sensitive files on the target system (e.g., ~/.ssh/id_rsa, .env files with API keys, database credentials, or proprietary model weights). They publish this model to a public hub such as Hugging Face or GitHub, or submit it through a model upload feature on a platform. A data scientist or automated CI/CD pipeline calls onnx.load('malicious_model.onnx')—the hardlink bypasses symlink detection entirely, and the target file contents become accessible through the model structure. In CI/CD pipelines with broad filesystem access, this could expose build secrets to an adversary who controls the model artifact.