Mlflow

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 image names into shell commands without proper sanitization, which are then executed using `os.system()`. This allows attackers to execute arbitrary commands by supplying malicious input through the `--container` parameter of the CLI. The issue affects environments where MLflow is used, including development setups, CI/CD pipelines, and cloud deployments.Show less

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 exploit a race condition and overwrite `.py` files in the virtual environment, leading to arbitrary code execution. The issue is resolved in version 3.4.0.Show less

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 and execute unauthorized calls against REST endpoints. An attacker can query, update, and delete experiments via the affected endpoints, leading to potential data exfiltration, destruction, or manipulation. The issue is resolved in version 3.5.0.Show less

MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26921.Show less

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. The specific flaw exists within the handling of passwords. The issue results from weak password requirements. An attacker can leverage this vulnerability to bypass authentication on the system. Was ZDI-CAN-26916.Show less

In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user account management. The issue is fixed in version 2.19.0.Show less

A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. This vulnerability allows an attacker to create a new account, which may be used to perform unauthorized actions on behalf of the malicious user.Show less

In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to respond to other requests. This vulnerability is due to uncontrolled resource consumption.Show less

A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. When users configure and use the dbfs service, concatenating the URL directly into the file protocol results in an arbitrary file read vulnerability. This issue occurs because only the path part of the URL is checked, while parts such as query and parameters are not handled. The vulnerability is triggered if the user has configured the dbfs service, and during usage, the service is mounted to a local directory.Show less

In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment name. This can cause the MLflow UI panel to become unresponsive, leading to a potential denial of service. Additionally, there is no character limit in the `artifact_location` parameter while creating the experiment.Show less

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.Show less

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.Show less

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.Show less

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. Specifically, when loading a dataset from a source URL with an HTTP scheme, the filename extracted from the `Content-Disposition` header or the URL path is used to generate the final file path without proper sanitization. This flaw enables an attacker to control the file path fully by utilizing path traversal or absolute path techniques, such as '../../tmp/poc.txt' or '/tmp/poc.txt', leading to arbitrary file write. Exploiting this vulnerability could allow a malicious user to execute commands on the vulnerable machine, potentially gaining access to data and model information. The issue is fixed in version 2.9.0.Show less

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.Show less

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.Show less

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.Show less

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.Show less