CVE-2023-2780
Lfprojects Mlflow ≤ 2.3.1
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2023-2780 is a critical-severity Path Traversal: '\..\filename' (CWE-29) 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 7% 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.
The vulnerability is a path traversal flaw (CWE-29) that accepts traversal sequences such as '\..\filename' and affects the open-source MLflow machine-learning platform in all versions prior to 2.3.1. The issue received a CVSS 3.1 base score of 9.8, reflecting network-accessible attack vectors that require no authentication or user interaction.
An unauthenticated remote attacker can supply crafted paths to read or write arbitrary files on the server, resulting in full compromise of confidentiality, integrity, and availability. Because the flaw is exposed through MLflow's network interfaces, any reachable instance is potentially reachable by an external adversary.
The maintainers resolved the issue in commit fae77a525dd908c56d6204a4cef1c1c75b4e9857, which shipped with MLflow 2.3.1; the referenced huntr.dev report confirms the same remediation. Operators should upgrade immediately and verify that no untrusted path inputs reach the affected endpoints.
The associated EPSS score has reached a peak of 0.8777 (current value 0.8614), indicating sustained and substantial exploitation interest since disclosure. As an ML-lifecycle tool, the affected component is commonly deployed in data-science and production environments, increasing the practical impact of successful attacks.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-0159
Vulnerability Data
Path Traversal: '\..\filename' 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
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 block this traversal vector.
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 can discover path-traversal flaws but does not itself prevent them in production code.
Application security requirements can mandate input validation and path canonicalization to block traversal sequences.
Secure architecture principles include directory sandboxing and safe file-access design that mitigate path traversal.
Secure coding standards directly require neutralizing path traversal sequences such as '\..\filename'.