CVE-2025-6507 is a critical vulnerability classified under CWE-502 (Deserialization of Untrusted Data) found in the h2oai/h2o-3 project, specifically affecting the latest master branch version 3.47.0.99999. The vulnerability stems from improper handling of deserialization processes where untrusted input data is processed without adequate validation. Attackers can exploit this flaw by bypassing the regular expression filters designed to prevent malicious parameter injection in JDBC connection strings. The bypass is achieved by manipulating spaces between parameters, which allows the injection of unauthorized commands or parameters. This leads to the potential for arbitrary code execution on the affected system and unauthorized reading of system files. The vulnerability compromises confidentiality, integrity, and availability, as attackers can execute arbitrary code and access sensitive files. The issue was addressed in version 3.46.0.8, indicating that versions after this patch are not vulnerable. The CVSS v3.0 score is 9.8, reflecting the high severity and ease of exploitation without requiring authentication or user interaction. No known exploits are currently reported in the wild, but the nature of the vulnerability makes it a prime target for attackers once weaponized.

For European organizations using h2oai/h2o-3, particularly in data science, machine learning, or AI-driven analytics environments, this vulnerability poses a significant risk. Successful exploitation could lead to full system compromise, unauthorized access to sensitive data, and disruption of critical AI workloads. Given the criticality of AI and data analytics in sectors such as finance, healthcare, manufacturing, and government within Europe, the impact could extend to data breaches, operational downtime, and regulatory non-compliance (e.g., GDPR violations). The ability to execute arbitrary code remotely without authentication increases the threat landscape, potentially allowing attackers to establish persistent footholds or move laterally within networks. The vulnerability could also undermine trust in AI systems and delay adoption of AI technologies in sensitive environments.

European organizations should immediately verify the version of h2oai/h2o-3 in use and upgrade to version 3.46.0.8 or later, where the vulnerability is patched. In addition to patching, organizations should implement strict network segmentation to limit access to systems running h2oai/h2o-3, especially restricting JDBC connection endpoints to trusted hosts. Employing runtime application self-protection (RASP) or web application firewalls (WAFs) with custom rules to detect and block suspicious JDBC parameter patterns could provide additional defense layers. Monitoring and logging deserialization activities and JDBC connection attempts can help detect exploitation attempts early. Organizations should also conduct code reviews and penetration testing focused on deserialization and input validation mechanisms in their AI/ML pipelines. Finally, applying the principle of least privilege to service accounts running h2oai/h2o-3 will reduce the potential impact of a successful exploit.