Search criteria
7 vulnerabilities found for onnx by onnx
CVE-2026-34447 (GCVE-0-2026-34447)
Vulnerability from cvelistv5 – Published: 2026-04-01 17:39 – Updated: 2026-04-01 19:14
VLAI?
Title
ONNX: External Data Symlink Traversal
Summary
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.
Severity ?
5.5 (Medium)
CWE
Assigner
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CVE-2026-34446 (GCVE-0-2026-34446)
Vulnerability from cvelistv5 – Published: 2026-04-01 17:37 – Updated: 2026-04-02 14:10
VLAI?
Title
ONNX: Arbitrary File Read via ExternalData Hardlink Bypass in ONNX load
Summary
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.
Severity ?
4.7 (Medium)
CWE
Assigner
References
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CVE-2026-27489 (GCVE-0-2026-27489)
Vulnerability from cvelistv5 – Published: 2026-04-01 17:33 – Updated: 2026-06-30 12:08
VLAI?
Title
ONNX: Path Traversal via Symlink
Summary
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to version 1.21.0, a path traversal vulnerability via symlink allows to read arbitrary files outside model or user-provided directory. This issue has been patched in version 1.21.0.
Severity ?
Assigner
References
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CVE-2026-34445 (GCVE-0-2026-34445)
Vulnerability from cvelistv5 – Published: 2026-04-01 17:30 – Updated: 2026-04-01 18:00
VLAI?
Title
ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings.
Summary
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 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.
Severity ?
8.6 (High)
CWE
Assigner
References
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CVE-2026-28500 (GCVE-0-2026-28500)
Vulnerability from cvelistv5 – Published: 2026-03-18 01:15 – Updated: 2026-06-30 12:07
VLAI?
Title
ONNX Untrusted Model Repository Warnings Suppressed by silent=True in onnx.hub.load() — Silent Supply-Chain Attack
Summary
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load() due to improper logic in the repository trust verification mechanism. While the function is designed to warn users when loading models from non-official sources, the use of the silent=True parameter completely suppresses all security warnings and confirmation prompts. This vulnerability transforms a standard model-loading function into a vector for Zero-Interaction Supply-Chain Attacks. When chained with file-system vulnerabilities, an attacker can silently exfiltrate sensitive files (SSH keys, cloud credentials) from the victim's machine the moment the model is loaded. As of time of publication, no known patched versions are available.
Severity ?
8.6 (High)
CWE
Assigner
References
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CVE-2024-27319 (GCVE-0-2024-27319)
Vulnerability from cvelistv5 – Published: 2024-02-23 17:39 – Updated: 2025-02-13 17:46
VLAI?
Summary
Versions of the package onnx before and including 1.15.0 are vulnerable to Out-of-bounds Read as the ONNX_ASSERT and ONNX_ASSERTM functions have an off by one string copy.
Severity ?
4.4 (Medium)
CWE
- CWE-125 - Out-of-bounds Read
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References
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CVE-2024-27318 (GCVE-0-2024-27318)
Vulnerability from cvelistv5 – Published: 2024-02-23 17:37 – Updated: 2025-02-13 17:46
VLAI?
Summary
Versions of the package onnx before and including 1.15.0 are vulnerable to Directory Traversal as the external_data field of the tensor proto can have a path to the file which is outside the model current directory or user-provided directory. The vulnerability occurs as a bypass for the patch added for CVE-2022-25882.
Severity ?
7.5 (High)
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
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