ReportizFlow
Filtered by vendor Lfprojects
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Total
60 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2024-1594 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7.5 High |
| 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. | ||||
| CVE-2024-1560 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.1 High |
| 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. | ||||
| CVE-2024-1558 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7.5 High |
| 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. | ||||
| CVE-2024-27134 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7 High |
| Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called. | ||||
| CVE-2024-1483 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7.5 High |
| 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. | ||||
| CVE-2024-37061 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run. | ||||
| CVE-2024-37060 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run. | ||||
| CVE-2024-37059 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37058 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37057 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37056 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37055 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37054 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37053 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2024-37052 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with. | ||||
| CVE-2023-2356 | 1 Lfprojects | 1 Mlflow | 2025-01-30 | 7.5 High |
| Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1. | ||||
| CVE-2023-30172 | 1 Lfprojects | 1 Mlflow | 2025-01-27 | 7.5 High |
| A directory traversal vulnerability in the /get-artifact API method of the mlflow platform up to v2.0.1 allows attackers to read arbitrary files on the server via the path parameter. | ||||
| CVE-2024-3848 | 1 Lfprojects | 1 Mlflow | 2025-01-24 | 7.5 High |
| A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909. The vulnerability arises from the application's handling of artifact URLs, where a '#' character can be used to insert a path into the fragment, effectively skipping validation. This allows an attacker to construct a URL that, when processed, ignores the protocol scheme and uses the provided path for filesystem access. As a result, an attacker can read arbitrary files, including sensitive information such as SSH and cloud keys, by exploiting the way the application converts the URL into a filesystem path. The issue stems from insufficient validation of the fragment portion of the URL, leading to arbitrary file read through path traversal. | ||||
| CVE-2023-2780 | 1 Lfprojects | 1 Mlflow | 2025-01-22 | 9.8 Critical |
| Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1. | ||||
| CVE-2024-27132 | 1 Lfprojects | 1 Mlflow | 2025-01-22 | 7.5 High |
| 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. | ||||