Bitnami package
mlflow
pkg:bitnami/mlflow
Vulnerabilities (75)
| CVE | Sev | CVSS | KEV | Affected versions | Fixed in | Published | Description |
|---|---|---|---|---|---|---|---|
| CVE-2024-37054 | Hig | 8.8 | >= 0.9.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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 | Hig | 8.8 | >= 1.1.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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 | Hig | 8.8 | >= 1.1.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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-2024-4263 | Med | 5.4 | < 2.12.1 | 2.12.1 | May 16, 2024 | A broken access control vulnerability exists in mlflow/mlflow versions before 2.10.1, where low privilege users with only EDIT permissions on an experiment can delete any artifacts. This issue arises due to the lack of proper validation for DELETE requests by users with EDIT perm | |
| CVE-2024-3848 | Hig | 7.5 | < 2.12.1 | 2.12.1 | May 16, 2024 | 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 fragmen | |
| CVE-2024-3573 | Cri | 9.3 | < 2.10.0 | 2.10.0 | Apr 16, 2024 | mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass checks and read arbitrary files on the system. The issue arises from the 'is_local_uri' function's failure to properly handle URIs with empty or 'file' schemes, | |
| CVE-2024-1594 | Hig | 7.5 | < 2.11.3 | 2.11.3 | Apr 16, 2024 | 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 rea | |
| CVE-2024-1593 | Hig | 7.5 | < 2.11.3 | 2.11.3 | Apr 16, 2024 | 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 o | |
| CVE-2024-1560 | Hig | 8.1 | < 2.12.2 | 2.12.2 | Apr 16, 2024 | 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 | |
| CVE-2024-1558 | Hig | 7.5 | < 2.12.1 | 2.12.1 | Apr 16, 2024 | 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 bypass | |
| CVE-2024-1483 | Hig | 7.5 | < 2.12.1 | 2.12.1 | Apr 16, 2024 | 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 ' | |
| CVE-2024-27133 | Hig | 7.5 | < 2.10.0 | 2.10.0 | Feb 23, 2024 | 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. | |
| CVE-2024-27132 | Hig | 7.5 | < 2.10.0 | 2.10.0 | Feb 23, 2024 | 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. | |
| CVE-2023-6977 | Hig | 7.5 | >= 1.0.0, < 2.9.2 | 2.9.2 | Dec 20, 2023 | This vulnerability enables malicious users to read sensitive files on the server. | |
| CVE-2023-6976 | Hig | 8.8 | < 2.9.2 | 2.9.2 | Dec 20, 2023 | This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process. | |
| CVE-2023-6975 | Cri | 9.8 | < 2.9.2 | 2.9.2 | Dec 20, 2023 | A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information. | |
| CVE-2023-6974 | Cri | 9.8 | < 2.9.2 | 2.9.2 | Dec 20, 2023 | 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. | |
| CVE-2023-6940 | Hig | 8.8 | < 2.9.2 | 2.9.2 | Dec 19, 2023 | with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system. | |
| CVE-2023-6909 | Hig | 7.5 | < 2.9.2 | 2.9.2 | Dec 18, 2023 | Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. | |
| CVE-2023-6831 | Hig | 8.1 | < 2.9.2 | 2.9.2 | Dec 15, 2023 | Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2. |
- affected >= 0.9.0, < 2.13.2fixed 2.13.2
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.
- affected >= 1.1.0, < 2.13.2fixed 2.13.2
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.
- affected >= 1.1.0, < 2.13.2fixed 2.13.2
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.
- affected < 2.12.1fixed 2.12.1
A broken access control vulnerability exists in mlflow/mlflow versions before 2.10.1, where low privilege users with only EDIT permissions on an experiment can delete any artifacts. This issue arises due to the lack of proper validation for DELETE requests by users with EDIT perm
- affected < 2.12.1fixed 2.12.1
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 fragmen
- affected < 2.10.0fixed 2.10.0
mlflow/mlflow is vulnerable to Local File Inclusion (LFI) due to improper parsing of URIs, allowing attackers to bypass checks and read arbitrary files on the system. The issue arises from the 'is_local_uri' function's failure to properly handle URIs with empty or 'file' schemes,
- affected < 2.11.3fixed 2.11.3
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 rea
- affected < 2.11.3fixed 2.11.3
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 o
- affected < 2.12.2fixed 2.12.2
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
- affected < 2.12.1fixed 2.12.1
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 bypass
- affected < 2.12.1fixed 2.12.1
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 '
- affected < 2.10.0fixed 2.10.0
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.
- affected < 2.10.0fixed 2.10.0
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.
- affected >= 1.0.0, < 2.9.2fixed 2.9.2
This vulnerability enables malicious users to read sensitive files on the server.
- affected < 2.9.2fixed 2.9.2
This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.
- affected < 2.9.2fixed 2.9.2
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
- affected < 2.9.2fixed 2.9.2
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.
- affected < 2.9.2fixed 2.9.2
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
- affected < 2.9.2fixed 2.9.2
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
- affected < 2.9.2fixed 2.9.2
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
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