Vulnerabilidades de Onnx
CVEs publicadas que afetam produtos de Onnx: 7 registro(s), com severidade, exploração e correções.
CVE-2026-49114
Alta · 7.1
CWE-22 21/08/2026
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-2026-11329
Baixa · 3.6
CWE-327 05/06/2026
onnx onnx-mlir Placeholder Node Cache backend.py generate_hash_key weak hash
A vulnerability has been found in onnx onnx-mlir up to 0.5.0.0. Affected by this issue is the function generate_hash_key of the file src/Runtime/python/torch_onnxmlir/src/torch_onnxmlir/backend.py of the component Placeholder Node Cache Handler. Such manipulation leads to use of weak hash. An attack has to be approached locally. A high complexity level is associated with this attack. The exploitation is known to be difficult. The name of the patch is 72c5187ff6d13c2c2b3d3789b8f5faf99f08a5b4. Applying a patch is advised to resolve this issue.
CVE-2026-34447
Média · 5.5
CWE-22 01/04/2026
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
Média · 5.5
CWE-22 01/04/2026
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
Alta · 8.6
CWE-20 01/04/2026
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
Alta · 7.5
CWE-23 01/04/2026
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
Crítica · 9.1
CWE-345 17/03/2026
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.