CVE-2024-10553
Published: 20 March 2025
Summary
CVE-2024-10553 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in H2O H2O. Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 13.4% of CVEs by exploit likelihood; 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-2025-7107
Vulnerability details
A vulnerability in the h2oai/h2o-3 REST API versions 3.46.0.4 allows unauthenticated remote attackers to execute arbitrary code via deserialization of untrusted data. The vulnerability exists in the endpoints POST /99/ImportSQLTable and POST /3/SaveToHiveTable, where user-controlled JDBC URLs are passed to…
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DriverManager.getConnection, leading to deserialization if a MySQL or PostgreSQL driver is available in the classpath. This issue is fixed in version 3.47.0.
- 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
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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
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.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.