CVE-2024-6960
Published: 21 July 2024
Summary
CVE-2024-6960 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Jfrog (inferred from references). Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation of Remote Services (T1210); ranked at the 40.0th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the Supply Chain and Deployment risk domain; MITRE ATLAS techniques in scope: AI Supply Chain Compromise (AML.T0010).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2430
Vulnerability details
The H2O machine learning platform uses "Iced" classes as the primary means of moving Java Objects around the cluster. The Iced format supports inclusion of serialized Java objects. When a model is deserialized, any class is allowed to be deserialized…
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(no class whitelist). An attacker can construct a crafted Iced model that uses Java gadgets and leads to arbitrary code execution when imported to the H2O platform.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- H2O is a machine learning platform for distributed ML workflows, fitting 'Other Platforms' as it handles model serialization/deserialization in cluster environments; vulnerability is AI-related due to exploitation via crafted ML models.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables arbitrary remote code execution through unsafe deserialization of crafted Iced models containing Java gadgets during cluster communication or model import, facilitating exploitation of remote services.
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.