CVE-2025-10952 is a medium-severity information disclosure vulnerability found in the geyang ml-logger product, specifically affecting the stream_handler function within the ml_logger/server.py file's File Handler component. The vulnerability arises from improper handling of the 'key' argument, which can be manipulated remotely without authentication or user interaction to disclose sensitive information. The flaw allows an attacker to remotely trigger the vulnerability by crafting requests that exploit the argument manipulation, potentially exposing internal data managed by the ml-logger service. The product employs continuous delivery with rolling releases, complicating precise version identification of affected or patched releases. The CVSS 4.0 base score is 6.9, reflecting network attack vector, low complexity, no privileges or user interaction required, and limited confidentiality impact without affecting integrity or availability. Although no known exploits are currently observed in the wild, the public release of exploit code increases the risk of exploitation. The vulnerability's root cause lies in insufficient validation or sanitization of the 'key' parameter in the stream_handler function, leading to unintended information leakage from the logging service. Given ml-logger's role in machine learning model logging and monitoring, exposure of internal logs or metadata could reveal sensitive operational or proprietary information.

For European organizations, the information disclosure vulnerability in ml-logger could lead to unauthorized access to sensitive operational data, including machine learning model logs, telemetry, or metadata. This exposure may compromise confidentiality of proprietary algorithms, training data insights, or system behavior patterns, potentially undermining competitive advantage or violating data protection regulations such as GDPR if personal data is indirectly exposed. While the vulnerability does not directly impact system integrity or availability, the leaked information could be leveraged by attackers for further targeted attacks or reconnaissance. Organizations relying on ml-logger for critical AI/ML workflows in sectors like finance, healthcare, or manufacturing may face increased risk of intellectual property theft or compliance violations. The remote, unauthenticated nature of the exploit increases the attack surface, particularly for deployments with exposed or poorly segmented logging endpoints. The lack of clear patch availability due to continuous delivery complicates timely remediation, potentially prolonging exposure.

To mitigate CVE-2025-10952 effectively, European organizations should: 1) Immediately audit and restrict network access to ml-logger endpoints, ensuring that stream_handler and related APIs are not exposed to untrusted networks or the internet. 2) Implement strict input validation and sanitization controls on the 'key' argument at the application or proxy level to prevent malicious manipulation. 3) Monitor logs and network traffic for unusual or unauthorized access patterns targeting ml-logger components. 4) Engage with the geyang vendor or community to obtain the latest patched versions or security advisories, given the continuous delivery model complicates version tracking. 5) Where possible, isolate ml-logger services within secure network segments and enforce strong authentication and authorization mechanisms to limit access. 6) Consider deploying Web Application Firewalls (WAFs) or API gateways with custom rules to detect and block exploit attempts targeting this vulnerability. 7) Conduct internal security reviews of ML infrastructure to identify any indirect exposure of sensitive data through logging services. 8) Prepare incident response plans to address potential data leakage scenarios stemming from this vulnerability.