CVE-2024-45853
Published: 12 September 2024
Summary
CVE-2024-45853 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 in the top 47.0% 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 Other Platforms; in the Data-Related Vulnerabilities risk domain; MITRE ATLAS techniques in scope: Hardware (AML.T0010.000), Invert AI Model (AML.T0024.001), Financial Harm (AML.T0048.000).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0111
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 used for a prediction.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- MindsDB is an AI platform for automating ML workflows, model deployment, and integrations with data sources including vector databases; the vulnerability involves deserialization of untrusted data in uploaded models and eval injections in handlers, core to the platform's AI functionality.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vulnerabilities enable authenticated arbitrary Python code execution via eval injections in database integrations (Weaviate, ChromaDB, SharePoint, vector DBs) and deserialization of malicious uploaded models, mapping to Python interpreter abuse, ingress tool transfer via model upload, and exploitation of the remote MindsDB service.
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.