CVE-2026-34447 — MEDIUM (CVSS 5.5)

CISO Take

Any pipeline or system loading untrusted ONNX model files is at risk of having sensitive host files read by a crafted model with symlinked external data — including secrets, SSH keys, or credentials accessible to the loading process. The attack surface is broad: CI/CD model validation pipelines, inference servers, and data scientists pulling models from public registries are all exposed. Patch to ONNX >= 1.21.0 immediately, and audit any workflow that loads ONNX models from external or untrusted sources.

What is the risk?

Medium severity on paper, but contextually higher in AI/ML environments. The CVSS 'local' vector is misleading: in ML pipelines, 'loading a model file' is routine and often automated — the attacker just needs to get a malicious ONNX file into the pipeline, which is trivial via public model hubs. No privileges required and the confidentiality impact is HIGH (full arbitrary file read as the process owner). In production environments, the inference service process may have access to API keys, database credentials, or internal certificates, making this a serious lateral movement enabler despite the medium score.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
onnx	pip	< 1.21.0	`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

5.5 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 5% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Local

AC Low

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. Run pip install --upgrade onnx and validate with pip show onnx.
DETECT

Scan existing ONNX model files in your artifact stores for symlinks — find <model_dir> -type l before loading any model.
SANDBOX

Run model loading in isolated containers with restricted filesystem mounts (no access to /etc, /home, secrets volumes).
SUPPLY CHAIN

Verify ONNX model file provenance via checksums/signatures before loading in any pipeline.
MONITOR

Alert on unexpected file access patterns from ML inference processes (e.g., reads to /etc/passwd, ~/.ssh, credential directories).
WORKAROUND (pre-patch): Pre-scan model external data files for symlinks before loading; reject any model that contains symlinked external data references.

CISA SSVC Assessment

Decision Track*

Exploitation poc

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.T0025 - Exfiltration via Cyber Means AML.T0037 - Data from Local System

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity of high-risk AI systems

ISO 42001

A.5.23 - Information security for use of cloud services / third-party AI components

NIST AI RMF

GOVERN 6.1 - Policies and procedures for AI supply chain risk management MANAGE 2.2 - Mechanisms to sustain AI risk management are in place

OWASP LLM Top 10

LLM03 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2026-34447?

Any pipeline or system loading untrusted ONNX model files is at risk of having sensitive host files read by a crafted model with symlinked external data — including secrets, SSH keys, or credentials accessible to the loading process. The attack surface is broad: CI/CD model validation pipelines, inference servers, and data scientists pulling models from public registries are all exposed. Patch to ONNX >= 1.21.0 immediately, and audit any workflow that loads ONNX models from external or untrusted sources.

Is CVE-2026-34447 actively exploited?

No confirmed active exploitation of CVE-2026-34447 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-34447?

1. PATCH: Upgrade onnx to >= 1.21.0 immediately. Run `pip install --upgrade onnx` and validate with `pip show onnx`. 2. DETECT: Scan existing ONNX model files in your artifact stores for symlinks — `find <model_dir> -type l` before loading any model. 3. SANDBOX: Run model loading in isolated containers with restricted filesystem mounts (no access to /etc, /home, secrets volumes). 4. SUPPLY CHAIN: Verify ONNX model file provenance via checksums/signatures before loading in any pipeline. 5. MONITOR: Alert on unexpected file access patterns from ML inference processes (e.g., reads to /etc/passwd, ~/.ssh, credential directories). 6. WORKAROUND (pre-patch): Pre-scan model external data files for symlinks before loading; reject any model that contains symlinked external data references.

What systems are affected by CVE-2026-34447?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, MLOps CI/CD pipelines, model registries, edge deployments.

What is the CVSS score for CVE-2026-34447?

CVE-2026-34447 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.02%.

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 a symlink traversal vulnerability in external data loading allows reading files outside the model directory. This issue has been patched in version 1.21.0.

Exploitation Scenario

An adversary publishes a seemingly legitimate ONNX model to a public hub (Hugging Face, ONNX Model Zoo mirror, or internal registry). The model contains a large external data file declared in the ONNX protobuf, but the actual file on disk is a symlink pointing to /etc/passwd, ~/.aws/credentials, or a Kubernetes service account token at /var/run/secrets/kubernetes.io/serviceaccount/token. A CI/CD pipeline pulling and validating the model, or a data scientist running `onnx.checker.check_model('model.onnx')`, triggers the external data load. The process reads the symlink target and — depending on the application — logs it, stores it, or returns it in an API response. Combined with a second-stage exfiltration channel, the attacker retrieves cloud credentials and pivots to the production environment.