Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2025-14287 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 29% 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 Machine Learning Libraries; 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-2025-14287 is a command injection vulnerability in mlflow/mlflow versions before v3.7.0, located in the `mlflow/sagemaker/__init__.py` file at lines 161-167. The flaw occurs due to the direct interpolation of user-supplied container image names into shell commands without sanitization, followed by execution via `os.system()`. Attackers can exploit this by providing malicious input through the `--container` parameter of the MLflow CLI, impacting environments such as development setups, CI/CD pipelines, and cloud deployments where MLflow is used for machine learning workflows.
The vulnerability has a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) and maps to CWE-94 (Improper Control of Generation of Code). Exploitation requires network access with low complexity and no privileges, but user interaction is needed, such as tricking a legitimate user into running the affected CLI command with a crafted `--container` argument. Successful exploitation enables arbitrary command execution on the host system, potentially leading to high confidentiality, integrity, and availability impacts.
Mitigation involves upgrading to mlflow/mlflow version v3.7.0 or later, where the issue is addressed. Additional details are available in the Huntr advisory at https://huntr.com/bounties/229cd526-41aa-4819-b6f0-e2d0371c89e3.
This vulnerability is particularly relevant to AI/ML practitioners, as MLflow is a widely used open-source platform for managing machine learning lifecycles, and exploitation could compromise sensitive training data or models in affected deployments. No public information on real-world exploitation is available as of the CVE publication on 2026-03-16.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208671
Vulnerability Data
A command injection vulnerability exists in mlflow/mlflow versions before v3.7.0, specifically in the `mlflow/sagemaker/__init__.py` file at lines 161-167. The vulnerability arises from the direct interpolation of user-supplied container image names into shell commands without proper sanitization, which are then executed…
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using `os.system()`. This allows attackers to execute arbitrary commands by supplying malicious input through the `--container` parameter of the CLI. The issue affects environments where MLflow is used, including development setups, CI/CD pipelines, and cloud deployments.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: mlflow, sagemaker
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.5V1.2.8V15.2.5V1.3.1
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.
Least functionality restricts available OS commands and interpreters, limiting the blast radius of injection.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Secure engineering principles require proper neutralization of untrusted input before command construction.
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).
Routine patching/maintenance can remediate known command-injection CVEs in dependencies (partial forward) but does nothing to stop developers from introducing improper neutralization in custom code (none reverse).
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.
Security testing and code review target insecure use of operating-system command interfaces, catching command-injection flaws introduced during development.
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.