A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. By smuggling path traversal sequences using the ';' character in URLs, attackers can manipulate the 'params' portion of the URL to gain unauthorized access to files or directories. This vulnerability allows for arbitrary data smuggling into the 'params' part of the URL, enabling attacks similar to those described in previous reports but utilizing the ';' character for parameter smuggling. Successful exploitation could lead to unauthorized information disclosure or server compromise.
A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the `artifact_location` parameter when creating an experiment. Attackers can exploit this vulnerability by using a fragment component `#` in the artifact location URI to read arbitrary files on the server in the context of the server's process. This issue is similar to CVE-2023-6909 but utilizes a different component of the URI to achieve the same effect.
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.
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.
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.
Minder is a Software Supply Chain Security Platform. In version 0.0.31 and earlier, it is possible for an attacker to register a repository with a invalid or differing upstream ID, which causes Minder to report the repository as registered, but not remediate any future changes which conflict with policy (because the webhooks for the repo do not match any known repository in the database). When attempting to register a repo with a different repo ID, the registered provider must have admin on the named repo, or a 404 error will result. Similarly, if the stored provider token does not have repo access, then the remediations will not apply successfully. Lastly, it appears that reconciliation actions do not execute against repos with this type of mismatch. This appears to primarily be a potential denial-of-service vulnerability. This vulnerability is patched in version 0.20240226.1425+ref.53868a8.
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.
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.
cdo-local-uuid project provides a specialized UUID-generating function that can, on user request, cause a program to generate deterministic UUIDs. An information leakage vulnerability is present in `cdo-local-uuid` at version `0.4.0`, and in `case-utils` in unpatched versions (matching the pattern `0.x.0`) at and since `0.5.0`, before `0.15.0`. The vulnerability stems from a Python function, `cdo_local_uuid.local_uuid()`, and its original implementation `case_utils.local_uuid()`.
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.