CVE-2025-10951 is a path traversal vulnerability identified in the geyang ml-logger software, specifically affecting the log_handler function within the ml_logger/server.py file. This vulnerability arises from improper validation or sanitization of the 'File' argument, allowing an attacker to manipulate the file path input to access files and directories outside the intended logging directory. The vulnerability can be exploited remotely without requiring authentication or user interaction, making it accessible to unauthenticated attackers over the network. The vulnerability is present in the version identified by commit acf255bade5be6ad88d90735c8367b28cbe3a743, but due to the product's rolling release model, exact versioning details for affected and patched releases are not clearly defined. The CVSS 4.0 base score is 6.9 (medium severity), reflecting the network attack vector, low attack complexity, no privileges or user interaction required, and limited impact on confidentiality, integrity, and availability. Exploitation could allow attackers to read arbitrary files on the server hosting ml-logger, potentially exposing sensitive configuration files, credentials, or other critical data. Although no known exploits are currently observed in the wild, a public exploit is available, increasing the risk of exploitation. The vulnerability does not appear to allow modification or deletion of files, but unauthorized file disclosure can lead to further attacks or information leakage. The lack of patch links suggests that users must monitor the vendor's updates closely or apply mitigations such as input validation or access controls themselves. Given ml-logger's role in machine learning logging and monitoring, compromised systems could lead to exposure of sensitive ML model data or operational logs, impacting data confidentiality and operational security.

For European organizations, the path traversal vulnerability in ml-logger poses a significant risk to the confidentiality of sensitive data stored or processed by machine learning infrastructure. Organizations relying on ml-logger for logging ML workflows, model training, or inference monitoring could have critical internal files exposed, including proprietary model data, user data, or credentials. This exposure could facilitate further attacks such as privilege escalation, lateral movement, or data exfiltration. The remote and unauthenticated nature of the exploit increases the threat surface, especially for organizations with publicly accessible ML logging endpoints. The medium severity rating indicates that while the vulnerability is not immediately catastrophic, it can lead to serious breaches if exploited in conjunction with other vulnerabilities or poor network segmentation. European organizations in sectors such as finance, healthcare, and critical infrastructure that utilize ML-logger or similar logging tools should be particularly vigilant, as data privacy regulations like GDPR impose strict requirements on data protection and breach notification. Failure to mitigate this vulnerability could result in regulatory penalties, reputational damage, and operational disruptions.

1. Immediate mitigation should include implementing strict input validation and sanitization on the 'File' parameter within the log_handler function to prevent path traversal sequences (e.g., '..', absolute paths). 2. Employ application-level access controls to restrict file access to only authorized directories and files, using allowlists rather than denylists. 3. Network-level protections such as firewall rules or API gateways should limit exposure of the ml-logger service to trusted internal networks or VPNs, reducing remote attack surface. 4. Monitor logs for suspicious access patterns or attempts to exploit path traversal, enabling early detection of exploitation attempts. 5. Since no official patches are currently linked, organizations should track vendor communications for updates and apply patches promptly once available. 6. Consider containerization or sandboxing of the ml-logger service to limit the impact of any successful exploitation. 7. Conduct regular security assessments and penetration testing focused on ML infrastructure to identify similar vulnerabilities. 8. Educate development and operations teams on secure coding practices related to file handling and input validation to prevent recurrence.