Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-2356 is a high-severity Relative Path Traversal (CWE-23) vulnerability in Lfprojects Mlflow. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data Obfuscation (T1001); ranked in the top 9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2023-2356 is a relative path traversal vulnerability (CWE-23) affecting the MLflow open-source machine-learning platform in the mlflow/mlflow GitHub repository prior to version 2.3.1. The flaw received a CVSS 3.1 base score of 7.5, reflecting network attackability with no required credentials or user interaction and a high impact on confidentiality.
An unauthenticated remote attacker can supply crafted path sequences to read arbitrary files on the server hosting the MLflow instance, exposing sensitive configuration data, credentials, or model artifacts without affecting integrity or availability.
The referenced GitHub commit and corresponding huntr.dev report document the fix that was merged to close the traversal issue; practitioners should upgrade to MLflow 2.3.1 or later. The associated EPSS score remains elevated (current 0.8902, peak 0.9049), indicating sustained exploitation interest for this ML-specific component.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-0158
Vulnerability Data
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V5.3.2
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.
Secure SDLC practices directly require input validation and path sanitization that prevent relative traversal.
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.