CVE-2023-6977
Published: 20 December 2023
Summary
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, ranked in the top 0.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
Deeper analysis
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 details
This vulnerability enables malicious users to read sensitive files on the server.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
No mitigating controls mapped yet. The per-CVE control annotator has not reached this CVE.