CVE-2025-51480 is a path traversal vulnerability identified in the ONNX (Open Neural Network Exchange) framework, specifically within the function onnx.external_data_helper.save_external_data in version 1.17.0. ONNX is widely used for representing machine learning models in a standardized format, facilitating interoperability between different AI frameworks. The vulnerability arises because the save_external_data function improperly handles the external_data.location parameter, allowing an attacker to supply crafted file paths containing directory traversal sequences (e.g., ../). This bypasses intended directory restrictions and enables the attacker to overwrite arbitrary files on the host filesystem. Such an overwrite can lead to modification or replacement of critical files, potentially resulting in code execution, privilege escalation, or denial of service. The vulnerability does not require authentication or user interaction, as it can be triggered by providing malicious input to the vulnerable function. Although no known exploits are currently reported in the wild, the flaw presents a significant risk given the widespread adoption of ONNX in AI pipelines and enterprise environments. The lack of a CVSS score indicates that the vulnerability is newly disclosed, and no official severity rating has been assigned yet.

For European organizations, the impact of this vulnerability can be substantial, especially for those relying on ONNX for AI model deployment, data science workflows, or machine learning operations. Successful exploitation could allow attackers to overwrite critical system or application files, leading to unauthorized code execution or disruption of AI services. This could compromise the confidentiality and integrity of sensitive data processed by AI models, including personal data protected under GDPR. Additionally, disruption of AI workflows could affect business continuity in sectors such as finance, healthcare, manufacturing, and automotive industries, where AI-driven decision-making is increasingly integral. Given the growing reliance on AI technologies in Europe, exploitation could also undermine trust in AI systems and cause regulatory and reputational damage. The vulnerability's ability to bypass directory restrictions increases the risk of widespread impact if attackers gain access to systems running vulnerable ONNX versions.

European organizations should immediately audit their environments to identify any deployments of ONNX version 1.17.0 or earlier where the save_external_data function is used. Since no patch links are currently available, organizations should consider the following mitigations: 1) Implement strict input validation and sanitization on any external_data.location parameters before they reach the vulnerable function to prevent directory traversal sequences. 2) Employ application-level sandboxing or containerization to limit the filesystem scope accessible to ONNX processes, reducing the risk of arbitrary file overwrites. 3) Monitor file integrity of critical system and application files to detect unauthorized modifications. 4) Restrict access to systems running ONNX to trusted users and networks to minimize attack surface. 5) Stay alert for official patches or updates from ONNX maintainers and apply them promptly once available. 6) Incorporate runtime detection tools that can identify anomalous file write operations indicative of exploitation attempts. These targeted mitigations go beyond generic advice by focusing on controlling input, limiting filesystem exposure, and enhancing detection capabilities specific to this vulnerability.