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onnx

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.

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.

Detailed information about CVE-2025-51480: n/a. Get real-time updates, technical details, and mitigation strategies.

CVE-2025-51480: n/a - Live Threat Intelligence - Threat Radar | OffSeq.com

CVE-2025-51480: n/a

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