CVE-2025-10769
Published: 21 September 2025
Summary
CVE-2025-10769 is a low-severity Improper Input Validation (CWE-20) vulnerability in H2O H2O. Its CVSS base score is 2.1 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 37.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-30378
Vulnerability details
A vulnerability has been found in h2oai h2o-3 up to 3.46.08. This affects an unknown function of the file /99/ImportSQLTable of the component H2 JDBC Driver. Such manipulation of the argument connection_url leads to deserialization. The attack may be launched…
more
remotely. The exploit has been disclosed to the public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: h2o
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization vulnerability (CWE-502) in H2 JDBC Driver's ImportSQLTable via connection_url enables remote code execution, facilitating exploitation of a public-facing application in the H2O-3 ML platform.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.