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

CVE-2026-34447 has a CVSS 3.1 base score of 5.5 (Medium). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N. EPSS exploitation probability: 0.0%.

Is there a patch available for CVE-2026-34447?

Yes, a patch is available for CVE-2026-34447. Update the affected software to the latest version immediately.

Microsoft CVE-2026-34447

EUVD-2026-17989 MEDIUM

UNIX Symbolic Link (Symlink) Following (CWE-61)

2026-04-01 GitHub_M

GHSA-p433-9wv8-28xj

Information Disclosure Microsoft Suse

5.5

CVSS 3.1 · GitHub Advisory

Severity by source

GitHub Advisory PRIMARY

5.5 MEDIUM

AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N

SUSE

MEDIUM

qualitative

Primary rating from GitHub Advisory.

CVSS VectorGitHub Advisory

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N

Attack Vector

Local

Attack Complexity

Low

Privileges Required

None

User Interaction

Required

Scope

Unchanged

Confidentiality

High

Integrity

None

Availability

None

Lifecycle Timeline

Patch released

Apr 02, 2026 - 02:30 nvd

Patch available

EUVD ID Assigned

Apr 01, 2026 - 18:15 euvd

EUVD-2026-17989

Analysis Generated

Apr 01, 2026 - 18:15 vuln.today

CVE Published

Apr 01, 2026 - 17:39 nvd

MEDIUM 5.5

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

17 pypi packages depend on onnx (9 direct, 8 indirect)

Ecosystem-wide dependent count for version 1.21.0.

DescriptionGitHub Advisory

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.

AnalysisAI

ONNX versions prior to 1.21.0 allow local attackers to read arbitrary files outside the model directory through symlink traversal during external data loading, requiring user interaction to load a malicious model file. The vulnerability has a CVSS score of 5.5 (medium severity) and is classified as information disclosure with confirmed patch availability in version 1.21.0.

Technical ContextAI

ONNX (Open Neural Network Exchange) is a machine learning model interchange format that supports external data storage. The vulnerability exists in the external data loading mechanism, which follows symlinks without proper validation (CWE-61: Improper Restriction of Rendered UI Layers or Frames). When processing ONNX model files, the implementation fails to restrict symlink traversal, allowing an attacker to craft a malicious model containing symlinks pointing to sensitive files outside the intended model directory. This affects local file access on systems where ONNX models are loaded, typically in machine learning frameworks and inference engines that consume ONNX format models.

RemediationAI

Vendor-released patch: ONNX version 1.21.0 and later. Update ONNX packages immediately to version 1.21.0 or newer through your package manager (pip install --upgrade onnx, or equivalent for your deployment method). For organizations unable to immediately patch, restrict external model loading to models from trusted sources only, validate model file integrity using cryptographic signatures before loading, and avoid loading ONNX models from user-uploaded sources or untrusted repositories. Additional mitigation includes running model inference in sandboxed environments with restricted filesystem access. Full details available in the GitHub security advisory at https://github.com/onnx/onnx/security/advisories/GHSA-p433-9wv8-28xj.