mlflow

vulnerable: 0.0.1 ... 1.9.1 (52 versions)

Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1.

vulnerable: 0.0.1 ... 2.2.1 (70 versions)

Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.2.2.

vulnerable: 0.0.1 ... 2.2.0 (69 versions)

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.

vulnerable: 0.0.1 ... 2.3.0 (72 versions)

Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.

vulnerable: 0.0.1 ... 2.3.0 (72 versions)

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1.

vulnerable: 0.0.1 ... 2.0.0rc0 (65 versions)

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.

vulnerable: 0.0.1 ... 2.4.2 (77 versions)

Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.

vulnerable: 0.0.1 ... 2.5.0 (78 versions)

OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.

vulnerable: 0.0.1 ... 2.8.1 (83 versions)

An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API.

vulnerable: 0.0.1 ... 2.7.1 (81 versions)

An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment.

vulnerable: 0.0.1 ... 2.8.0 (82 versions)

MLflow allowed arbitrary files to be PUT onto the server.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

An attacker can overwrite any file on the server hosting MLflow without any authentication.

vulnerable: 0.0.1 ... 2.9.0 (84 versions)

A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Ty…

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

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.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.

vulnerable: 0.0.1 ... 2.9.1 (85 versions)

This vulnerability enables malicious users to read sensitive files on the server.

vulnerable: 0.0.1 ... 2.8.1 (83 versions)

A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command ('Command Injection') within the `mlflow.data.http_dataset_source.py` module. Specific…

vulnerable: 0.0.1 ... 2.9.2 (95 versions)

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…

vulnerable: 0.0.1 ... 2.9.2 (95 versions)

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 …

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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_…

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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 U…

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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 componen…

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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 templat…

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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 ove…

vulnerable: 0.0.1 ... 2.9.2 (109 versions)

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 t…

vulnerable: 0.0.1 ... 2.9.2 (92 versions)

A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. This vulnerability arises from the application's failure to properly validate URI fragments for d…

vulnerable: 0.0.1 ... 2.9.2 (92 versions)

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the i…

vulnerable: 0.0.1 ... 2.9.2 (86 versions)

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 …

vulnerable: 1.1.0 ... 2.9.2 (83 versions)

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.

vulnerable: 1.1.0 ... 2.9.2 (83 versions)

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.

vulnerable: 0.9.0 ... 2.9.2 (87 versions)

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.

vulnerable: 1.24.0 ... 2.9.2 (50 versions)

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.

vulnerable: 1.23.0 ... 2.9.2 (52 versions)

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 wit…

vulnerable: 2.0.0 ... 2.9.2 (40 versions)

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.

vulnerable: 2.10.0 ... 2.9.2 (26 versions)

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 wi…

vulnerable: 0.5.0 ... 3.4.0rc0 (149 versions)

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.

vulnerable: 1.27.0 ... 2.9.2 (45 versions)

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.

vulnerable: 1.11.0 ... 2.9.2 (65 versions)

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.

vulnerable: 0.0.1 ... 2.9.2 (95 versions)

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 …

vulnerable: 0.0.1 ... 2.9.2 (87 versions)

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 fo…

vulnerable: 0.0.1 ... 3.3.2 (155 versions)

In mlflow version 2.20.3, the temporary directory used for creating Python virtual environments is assigned insecure world-writable permissions (0o777). This vulnerability allows an attacker with write access to the `/tmp` directory to exp…

vulnerable: 0.0.1 ... 2.9.2 (131 versions)

MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. …

vulnerable: 0.0.1 ... 3.5.0rc0 (158 versions)

MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections an…

vulnerable: 0.0.1 ... 3.7.0rc0 (164 versions)

A command injection vulnerability exists in mlflow/mlflow versions before v3.7.0, specifically in the `mlflow/sagemaker/__init__.py` file at lines 161-167. The vulnerability arises from the direct interpolation of user-supplied container i…