Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2025-51480 is a high-severity Path Traversal (CWE-22) vulnerability in Linuxfoundation Onnx. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th 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 Deep Learning Frameworks; in the Supply Chain and Deployment 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-51480 is a path traversal vulnerability affecting ONNX version 1.17.0, specifically in the onnx.external_data_helper.save_external_data function. Attackers can exploit it by supplying crafted external_data.location paths containing traversal sequences, which bypass intended directory restrictions and enable overwriting of arbitrary files. The issue carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) and maps to CWE-22; it was published on 2025-07-22.
The vulnerability can be exploited by remote attackers requiring no privileges, but it demands user interaction, such as convincing a victim to load or process a malicious ONNX model file. Successful exploitation allows arbitrary file overwrites on the target system, resulting in high impacts to confidentiality, integrity, and availability, depending on the overwritten files' locations and privileges of the affected process.
Advisories and patches are detailed in the GitHub security advisory GHSA-6rq9-53c3-f7vj, the ONNX repository, and pull requests #6959 and #7040, which address the issue. Further analysis appears in a Gecko Security blog post at https://www.gecko.security/blog/cve-2025-51480. Security practitioners should apply these updates to mitigate risks in ONNX deployments.
ONNX serves as an open format for machine learning model interoperability, heightening the vulnerability's relevance to AI/ML pipelines involving external data handling. No details on real-world exploitation are available in the provided information.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-22338
Vulnerability Data
Path Traversal vulnerability in onnx.external_data_helper.save_external_data in ONNX 1.17.0 allows attackers to overwrite arbitrary files by supplying crafted external_data.location paths containing traversal sequences, bypassing intended directory restrictions.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: onnx
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
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.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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.