CVE-2024-45758
Published: 06 September 2024
Summary
CVE-2024-45758 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in H2O H2O. Its CVSS base score is 9.1 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 28.2th 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 Other Platforms; in the Other ATLAS/OWASP Terms risk domain; MITRE ATLAS techniques in scope: AI Model Inference API Access (AML.T0040), Exfiltration via AI Inference API (AML.T0024), External Harms (AML.T0048).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-41584
Vulnerability details
H2O.ai H2O through 3.46.0.4 allows attackers to arbitrarily set the JDBC URL, leading to deserialization attacks, file reads, and command execution. Exploitation can occur when an attacker has access to post to the ImportSQLTable URI with a JSON document containing…
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a connection_url property with any typical JDBC Connection URL attack payload such as one that uses queryInterceptors.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- H2O.ai H2O is an open-source distributed machine learning platform supporting data processing, model training, and deployment in AI/ML workflows, fitting the 'Other Platforms' category.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unauthenticated exploitation of public-facing ImportSQLTable endpoint via malicious JDBC URL enables remote code execution (T1190, T1210), command execution (T1059), and file reads (T1005, T1083).
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
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.