CVE-2025-6544 in h2o-3
Summary
by MITRE • 09/21/2025
A deserialization vulnerability exists in h2oai/h2o-3 versions <= 3.46.0.8, allowing attackers to read arbitrary system files and execute arbitrary code. The vulnerability arises from improper handling of JDBC connection parameters, which can be exploited by bypassing regular expression checks and using double URL encoding. This issue impacts all users of the affected versions.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 09/21/2025
The vulnerability identified as CVE-2025-6544 represents a critical deserialization flaw within the h2oai/h2o-3 machine learning platform, specifically affecting versions up to and including 3.46.0.8. This security weakness stems from inadequate input validation and sanitization mechanisms within the JDBC connection parameter handling subsystem, creating a pathway for remote attackers to exploit the system through carefully crafted malicious inputs. The flaw operates at the intersection of multiple security domains, fundamentally compromising the integrity and confidentiality of systems running the affected software. Organizations utilizing this platform for machine learning operations and data processing face significant risk exposure due to the severity of potential impacts.
The technical exploitation mechanism leverages improper handling of JDBC connection parameters, where the software fails to properly validate and sanitize user-supplied input before processing. Attackers can bypass regular expression checks through sophisticated techniques involving double URL encoding, which allows malicious payloads to be injected into the deserialization chain without triggering basic security filters. This bypass technique demonstrates a fundamental flaw in the input validation architecture, where the security controls are easily circumvented through encoding manipulation. The vulnerability operates under the CWE-502 category, which specifically addresses deserialization of untrusted data, making it a prime target for attackers seeking to execute arbitrary code on vulnerable systems. The attack vector typically involves crafting malicious JDBC connection strings that contain serialized objects designed to trigger code execution upon deserialization.
The operational impact of CVE-2025-6544 extends beyond simple code execution capabilities to encompass complete system compromise and data exfiltration. Successful exploitation enables attackers to read arbitrary system files, potentially accessing sensitive configuration data, authentication credentials, and proprietary machine learning models. This vulnerability aligns with ATT&CK technique T1059.007 for command and scripting interpreter and T1074.001 for data staging, as attackers can establish persistent access and exfiltrate valuable information. The attack surface includes all users and administrators who interact with the h2o-3 platform, making it particularly dangerous in enterprise environments where machine learning workloads process sensitive data. Organizations may experience unauthorized access to their data science infrastructure, leading to potential intellectual property theft, regulatory compliance violations, and business disruption.
Mitigation strategies for CVE-2025-6544 require immediate action to upgrade to patched versions of h2oai/h2o-3, specifically versions beyond 3.46.0.8 where the deserialization vulnerability has been addressed. Organizations should implement network segmentation and access controls to limit exposure of vulnerable systems to untrusted networks. Security teams must conduct comprehensive vulnerability assessments to identify any systems running affected versions and establish monitoring procedures for suspicious deserialization activities. The remediation process should include disabling unnecessary JDBC connection functionality where possible and implementing strict input validation measures that prevent malicious payloads from reaching the deserialization layer. Additionally, organizations should consider implementing application-level firewalls and intrusion detection systems specifically configured to detect and block double URL encoding patterns commonly used in exploitation attempts. Regular security updates and patch management procedures should be enforced to prevent similar vulnerabilities from emerging in other components of the machine learning ecosystem.