Linuxfoundation Onnx vulnerabilities

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Published CVEs affecting Linuxfoundation Onnx: 7 record(s), with affected versions and fixes.
cpe:2.3:a:linuxfoundation:onnx

CVE-2026-49114 High · 7.1 CWE-22 Aug 21, 2026

ONNX symlink-following and path-traversal arbitrary file write

In ONNX before 1.21.0, the 'save_external_data' function builds the external-data file path from the model's external_data location field and opens it for writing without 'O_NOFOLLOW/O_EXCL', after a non-atomic 'os.path.isfile()' check. A local attacker with write access to the directory where a victim serializes external data can deterministically pre-plant a symlink that is being followed, causing the victim's write to append to any file the victim can write, e.g. ~/.ssh/authorized_keys, cron files, or application configs. Fixed in 1.21.0.

CVE-2026-44512 Medium · 5.5 CWE-476 Jul 8, 2026

ONNX: Null Pointer Dereference in Upsample Version Converter Adapter (Zero Inputs)

Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.9.0 before 1.22.0, onnx.version_converter.convert_version() can dereference a null pointer in Upsample_6_7::adapt_upsample_6_7() in onnx/version_converter/adapters/upsample_6_7.h when processing an untrusted model with an Upsample node that has zero inputs, causing an unrecoverable denial of service. This issue is fixed in version 1.22.0.

CVE-2026-34447 Medium · 5.5 CWE-22 Apr 1, 2026

ONNX: External Data Symlink Traversal

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.

CVE-2026-34446 Medium · 5.5 CWE-22 Apr 1, 2026

ONNX: Arbitrary File Read via ExternalData Hardlink Bypass in ONNX load

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.

CVE-2026-34445 High · 8.6 CWE-20 Apr 1, 2026

ONNX: Malicious ONNX models can crash servers by exploiting unprotected object settings.

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.

CVE-2026-27489 High · 7.5 CWE-23 Apr 1, 2026

ONNX: Path Traversal via Symlink

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.

CVE-2026-28500 Critical · 9.1 CWE-345 Mar 17, 2026

ONNX Untrusted Model Repository Warnings Suppressed by silent=True in onnx.hub.load() — Silent Supply-Chain Attack

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.