CVE-2025-6507 is a critical security vulnerability identified in the h2oai/h2o-3 open-source machine learning platform, specifically affecting the latest master branch version 3.47.0.99999. The root cause of this vulnerability lies in the deserialization of untrusted data (CWE-502), a common and dangerous security flaw where untrusted input is deserialized without sufficient validation or sanitization. This flaw enables attackers to execute arbitrary code and read sensitive system files remotely without authentication or user interaction. The vulnerability exploits a weakness in the input validation mechanism designed to filter malicious parameters in JDBC connection strings. Attackers can bypass the regular expression filters by manipulating spaces between parameters, effectively injecting malicious payloads that the system fails to detect. This bypass allows unauthorized access to system files and arbitrary code execution within the context of the vulnerable application. The vulnerability is rated with a CVSS v3.0 score of 9.8 (critical), reflecting its high impact on confidentiality, integrity, and availability, combined with its ease of exploitation over a network without any privileges or user interaction. Although the vulnerability is present in the latest master branch version 3.47.0.99999, it is reportedly addressed in version 3.46.0.8, indicating that users running versions prior to the fix remain at risk. No known exploits are currently reported in the wild, but the severity and nature of the flaw suggest that exploitation could lead to full system compromise, data leakage, and disruption of machine learning services relying on h2o-3.

For European organizations, the impact of CVE-2025-6507 can be significant, especially for those relying on h2oai/h2o-3 for data analytics, machine learning, and AI-driven decision-making processes. Successful exploitation could lead to unauthorized disclosure of sensitive data, including intellectual property, personal data protected under GDPR, and proprietary machine learning models. The arbitrary code execution capability could allow attackers to deploy malware, establish persistent backdoors, or disrupt critical AI services, potentially affecting business continuity and trust. Given the critical nature of the vulnerability and the lack of required authentication, attackers can remotely compromise systems, increasing the risk of widespread attacks. Organizations in sectors such as finance, healthcare, manufacturing, and government, which increasingly depend on AI platforms, may face regulatory penalties, reputational damage, and operational disruptions if exploited. Moreover, the ability to read system files could expose configuration details and credentials, facilitating further lateral movement within networks.

European organizations should immediately verify their use of h2oai/h2o-3 and identify affected versions. The primary mitigation is to upgrade to version 3.46.0.8 or later, where the vulnerability has been addressed. If upgrading is not immediately feasible, organizations should implement strict network segmentation and firewall rules to restrict access to h2o-3 services, limiting exposure to trusted internal networks only. Additionally, monitoring and logging of JDBC connection attempts should be enhanced to detect anomalous parameter patterns indicative of exploitation attempts. Employing Web Application Firewalls (WAFs) with custom rules to detect and block suspicious input patterns targeting JDBC parameters can provide an additional layer of defense. Organizations should also conduct thorough code reviews and penetration testing focused on deserialization and input validation weaknesses within their AI platforms. Finally, applying the principle of least privilege to service accounts running h2o-3 can reduce the impact of a successful exploit.