CVE-2024-0960
Published: 27 January 2024
Summary
CVE-2024-0960 is a medium-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Flink-Extended Aiflow. Its CVSS base score is 5.0 (Medium).
Operationally, ranked at the 23.8th 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 AI Platforms.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0285
Vulnerability details
A vulnerability was found in flink-extended ai-flow 0.3.1. It has been declared as critical. Affected by this vulnerability is the function cloudpickle.loads of the file \ai_flow\cli\commands\workflow_command.py. The manipulation leads to deserialization. The attack can be launched remotely. The complexity of…
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an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The identifier VDB-252205 was assigned to this vulnerability.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
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.