CVE-2024-45855
Published: 12 September 2024
Summary
CVE-2024-45855 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Mindsdb Mindsdb. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Python (T1059.006); ranked at the 45.4th 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 Supply Chain and Deployment risk domain; MITRE ATLAS techniques in scope: AI Supply Chain Compromise (AML.T0010), Obtain Capabilities (AML.T0016), Exfiltration via AI Inference API (AML.T0024).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0113
Vulnerability details
Deserialization of untrusted data can occur in versions 23.10.2.0 and newer of the MindsDB platform, enabling a maliciously uploaded ‘inhouse’ model to run arbitrary code on the server when using ‘finetune’ on it.
- 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
- MindsDB is an open-source AI/ML platform for AutoML, model integration, and federated learning in databases, with vulnerabilities in model deserialization during finetuning and unsafe eval in AI integrations like vector databases.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vulnerabilities enable authenticated arbitrary Python code execution via eval injection in integrations (Weaviate, ChromaDB, SharePoint, vector DBs) and deserialization during model finetuning, facilitating Python interpreter abuse and remote service exploitation.
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.