Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:NSummary
CVE-2026-34447 is a medium-severity Path Traversal (CWE-22) vulnerability in Linuxfoundation Onnx. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 16th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-17989
Vulnerability Data
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…
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version 1.21.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: machine learning, neural network, onnx
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Symlink/path traversal in external data loading directly enables arbitrary local file reads outside the model directory.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Validates external data file paths during ONNX model loading to block traversal sequences and symlink targets outside the intended directory.
Enforces file-access policy so the ONNX loader may only open paths that resolve inside the model directory, directly stopping symlink traversal reads.
Restricts the ONNX process to the minimal set of directories and files, limiting the impact of any successful path traversal or symlink follow.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent introduction of symlink-following flaws in file-handling code.
Vulnerability identification can discover existing symlink issues but does not prevent or remediate them in code.
Least-privilege access policies can limit damage from symlink attacks but do not address the coding flaw itself.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.