CVE-2024-37059
Published: 04 June 2024
Summary
CVE-2024-37059 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Lfprojects Mlflow. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked in the top 36.5% 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 Supply Chain and Deployment risk domain; MITRE ATLAS techniques in scope: AI Supply Chain Compromise (AML.T0010).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2191
Vulnerability details
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with.
- 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
- MLflow is an open-source platform for managing the ML lifecycle, including model tracking, logging, and loading. The vulnerability involves deserialization of untrusted data in model loading functions (e.g., PyTorch, sklearn, pyfunc), enabling RCE, which is core to AI/ML workflows.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization vulnerabilities in MLflow model loading functions (e.g., sklearn, pyfunc, pmdarima, lightgbm, PyTorch) enable arbitrary remote code execution on a victim's local system when loading maliciously uploaded models, directly facilitating exploitation for client execution.
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.