Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-11201 is a critical-severity Path Traversal (CWE-22) vulnerability in Lfprojects Mlflow. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) 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-2025-11201 is a remote code execution vulnerability affecting the MLflow Tracking Server, stemming from a directory traversal flaw (CWE-22) in the handling of model file paths during model creation. The issue arises due to insufficient validation of user-supplied paths before they are used in file operations, allowing attackers to manipulate paths arbitrarily. This critical vulnerability, assigned a CVSS v3.1 score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), impacts affected installations of the MLflow Tracking Server and was previously tracked as ZDI-CAN-26921.
Remote attackers can exploit this vulnerability over the network without authentication, requiring no user interaction or privileges. Successful exploitation enables the execution of arbitrary code in the context of the service account running the MLflow Tracking Server, potentially leading to full system compromise including high confidentiality, integrity, and availability impacts.
Advisories and patches provide mitigation guidance: a fix is implemented in MLflow via the commit at https://github.com/B-Step62/mlflow/commit/2e02bc7bb70df243e6eb792689d9b8eba0013161, and detailed analysis is available in the Zero Day Initiative advisory at https://www.zerodayinitiative.com/advisories/ZDI-25-931/.
MLflow's role in managing machine learning experiment tracking and model deployment introduces AI/ML relevance, as vulnerable tracking servers could expose ML workflows to remote code execution risks.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-36707
Vulnerability Data
MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within…
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the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26921.
- 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
- Matched keywords: mlflow
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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 in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.