Lfprojects: >> Mlflow >> 0.5.1 Security Vulnerabilities
A path traversal vulnerability exists in mlflow/mlflow version 2.9.2, allowing attackers to access arbitrary files on the server. By crafting a series of HTTP POST requests with specially crafted 'artifact_location' and 'source' parameters, using a local URI with '#' instead of '?', an attacker can traverse the server's directory structure. The issue occurs due to insufficient validation of user-supplied input in the server's handlers.
CVSS Score
7.5
EPSS Score
0.733
Published
2024-04-16
A path traversal vulnerability exists in the `_create_model_version()` function within `server/handlers.py` of the mlflow/mlflow repository, due to improper validation of the `source` parameter. Attackers can exploit this vulnerability by crafting a `source` parameter that bypasses the `_validate_non_local_source_contains_relative_paths(source)` function's checks, allowing for arbitrary file read access on the server. The issue arises from the handling of unquoted URL characters and the subsequent misuse of the original `source` value for model version creation, leading to the exposure of sensitive files when interacting with the `/model-versions/get-artifact` handler.
CVSS Score
7.5
EPSS Score
0.001
Published
2024-04-16
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Attackers can bypass path validation by exploiting the double decoding process in the `_delete_artifact_mlflow_artifacts` handler and `local_file_uri_to_path` function, allowing for the deletion of arbitrary directories on the server's filesystem. This vulnerability is due to an extra unquote operation in the `delete_artifacts` function of `local_artifact_repo.py`, which fails to properly sanitize user-supplied paths. The issue is present up to version 2.9.2, despite attempts to fix a similar issue in CVE-2023-6831.
CVSS Score
8.1
EPSS Score
0.001
Published
2024-04-16
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe.
This issue leads to a client-side RCE when running an untrusted recipe in Jupyter Notebook.
The vulnerability stems from lack of sanitization over template variables.
CVSS Score
7.5
EPSS Score
0.003
Published
2024-02-23
Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields.
CVSS Score
7.5
EPSS Score
0.003
Published
2024-02-23
A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abuse to get a remote code execution on the victim machine.
CVSS Score
8.6
EPSS Score
0.026
Published
2023-12-20
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
CVSS Score
9.8
EPSS Score
0.015
Published
2023-12-20
This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.
CVSS Score
8.8
EPSS Score
0.001
Published
2023-12-20
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
CVSS Score
9.0
EPSS Score
0.001
Published
2023-12-19
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
CVSS Score
7.5
EPSS Score
0.862
Published
2023-12-18