CVE-2025-10951 is a path traversal vulnerability identified in the 'ml-logger' component of the 'geyang' project, specifically affecting the log_handler function in the ml_logger/server.py file. The vulnerability arises from improper validation or sanitization of the 'File' argument passed to the log_handler function, allowing an attacker to manipulate the file path input. This manipulation enables traversal outside the intended directory structure, potentially granting unauthorized access to arbitrary files on the server's filesystem. The vulnerability can be exploited remotely without any authentication or user interaction, increasing its risk profile. The product uses a rolling release model for continuous delivery, which complicates precise versioning information for affected and patched releases. The CVSS v4.0 base score is 6.9 (medium severity), reflecting a network attack vector with low complexity, no privileges or user interaction required, and limited impact on confidentiality, integrity, and availability. Although there are no known exploits in the wild at the time of publication, a public exploit is available, which could facilitate exploitation by attackers. The vulnerability's impact depends on the server's file system structure and the privileges of the application process, potentially exposing sensitive configuration files, logs, or other critical data. This vulnerability is particularly concerning for environments where ml-logger is used to collect and manage machine learning logs, as unauthorized file access could lead to data leakage or further system compromise.

For European organizations, the impact of CVE-2025-10951 could be significant if they utilize the geyang ml-logger for machine learning operations or logging infrastructure. Unauthorized file access via path traversal could lead to exposure of sensitive data, including intellectual property, personal data protected under GDPR, or internal configuration files that could facilitate further attacks. The ability to exploit this vulnerability remotely without authentication increases the risk of widespread exploitation, especially in organizations with exposed or poorly segmented logging servers. This could result in data breaches, regulatory penalties, operational disruption, and reputational damage. Organizations in sectors with high reliance on machine learning and data analytics, such as finance, healthcare, and manufacturing, may face elevated risks. Additionally, the lack of clear patching information due to the rolling release model complicates timely remediation, potentially prolonging exposure. The medium severity rating suggests that while the vulnerability is serious, it may not directly lead to full system compromise but can serve as a stepping stone for attackers to escalate privileges or move laterally within networks.

To mitigate CVE-2025-10951, European organizations should implement the following specific measures: 1) Immediately audit all deployments of geyang ml-logger to identify affected versions, focusing on the commit hash acf255bade5be6ad88d90735c8367b28cbe3a743 or earlier. 2) Apply any available patches or updates from the vendor as soon as they are released; if no official patch is available, consider temporarily disabling or isolating the ml-logger service from external networks to prevent remote exploitation. 3) Implement strict input validation and sanitization on the 'File' argument within the log_handler function, ensuring that file paths are normalized and constrained to intended directories to prevent traversal. 4) Employ network segmentation and firewall rules to restrict access to logging servers, limiting exposure to trusted internal networks only. 5) Monitor logs and network traffic for suspicious file access patterns or anomalous requests targeting the logging service. 6) Conduct a thorough review of file permissions and access controls on servers running ml-logger to minimize the impact of potential traversal attacks. 7) Educate development and operations teams about secure coding practices related to file handling and path validation to prevent similar vulnerabilities in the future.