CVE-2023-6977
Lfprojects Mlflow 1.0.0 – 2.9.2
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2023-6977 is a high-severity Path Traversal: '\..\filename' (CWE-29) vulnerability in Lfprojects Mlflow. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 11% 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-2023-6977 is a path traversal vulnerability (CWE-29) affecting the MLflow machine learning platform. It permits unauthorized reading of arbitrary files on the server hosting the application, as reflected in its CVSS 7.5 rating for network-accessible confidentiality impact without authentication.
An unauthenticated attacker with network reachability to an MLflow instance can exploit the flaw to retrieve sensitive server-side files. The attack requires no user interaction and directly compromises data confidentiality while leaving integrity and availability untouched.
Public references point to a fix merged in commit 4bd7f27c810ba7487d53ed5ef1038fca0f8dc28c of the mlflow/mlflow repository, along with associated hunter bounties that document the issue. Applying this patch or an equivalent update closes the traversal vector.
The vulnerability is relevant to AI/ML environments because MLflow is widely used to manage experiments, models, and artifacts. Its EPSS score remains elevated, with a current value of 0.8304 and a recorded peak of 0.8618.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-3270
Vulnerability Data
This vulnerability enables malicious users to read sensitive files on the server.
- 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'.