Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

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.

Denial of Service and Data Model Poisoning via URL Encoding in mlflow/mlflow

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 intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.

Local File Inclusion (LFI) via URI Fragment Parsing in mlflow/mlflow

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 directory traversal sequences such as '../'. An attacker can exploit this flaw by manipulating the fragment part of the URI to read arbitrary files on the local file system, including sensitive files like '/etc/passwd'. The vulnerability is a bypass to a previous patch that only addressed similar manipulation within the URI's query string, highlighting the need for comprehensive validation of all parts of a URI to prevent LFI attacks.

Path Traversal: '\..\filename'

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

Unrestricted Upload of File with Dangerous Type

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

Path Traversal: '\..\filename'

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

Server-Side Request Forgery (SSRF)

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.

Path Traversal: '\..\filename' in mlflow/mlflow

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

Path Traversal: '\..\filename' in mlflow/mlflow

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

Path Traversal in mlflow/mlflow

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

Improper Neutralization of Special Elements Used in a Template Engine in mlflow/mlflow

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

Reflected XSS via Content-Type Header in mlflow/mlflow

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-Type header, which is then improperly reflected back to the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/__init__.py file, where the user-supplied Content-Type header is directly injected into a Python formatted string and returned to the user, facilitating the XSS attack.

MLflow Arbitrary File Write

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

MLflow Arbitrary File Upload

MLflow allowed arbitrary files to be PUT onto the server.

MLflow Authentication Bypass

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

OS Command Injection in mlflow/mlflow

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

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.

Absolute Path Traversal in mlflow/mlflow

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

Insecure Temporary File in mlflow/mlflow

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