CVE-2024-37061
RCE in Lfprojects Mlflow ≥ 1.11.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2024-37061 is a high-severity Code Injection (CWE-94) vulnerability in Lfprojects Mlflow. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 44% 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 AI Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-37061 is a remote code execution vulnerability affecting the MLflow platform in versions 1.11.0 and newer. It stems from improper control of code generation, tracked as CWE-94, and carries a CVSS 3.1 score of 8.8. The flaw allows a maliciously crafted MLproject file to trigger arbitrary code execution on an end user's system when the project is run.
An attacker can supply the crafted MLproject to a victim, who then executes it through MLflow; because the vector requires no privileges and only routine user interaction, the attacker achieves full code execution with impacts to confidentiality, integrity, and availability on the target system.
The EPSS score for this CVE rose from a low baseline to a peak of 0.0736 on 2025-12-11 before receding to the current value of 0.0395, indicating post-disclosure exploitation interest. Public advisories from HiddenLayer detail the issue and are available at the referenced URLs.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2125
Vulnerability Data
Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI 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 logging, tracking, and loading. The vulnerability involves RCE via malicious MLproject files or deserialization in model loaders (e.g., sklearn, pyfunc, pmdarima, lightgbm integrations), confirming it affects an AI/ML platform.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.