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.httpdatasetsource.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.

Impact

Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the mlflow server or mlflow ui commands using an MLflow version older than MLflow 2.2.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).

This issue only affects users and integrations that run the mlflow server and mlflow ui commands. Integrations that do not make use of mlflow server or mlflow ui are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.

The vulnerability detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1177 enables an actor to download arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to which the host server has access.

Patches This vulnerability has been patched in MLflow 2.2.1, which was released to PyPI on March 2nd, 2023. If you are using mlflow server or mlflow ui with the MLflow Model Registry, we recommend upgrading to MLflow 2.2.1 as soon as possible.

Workarounds If you are using the MLflow open source mlflow server or mlflow ui commands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.

If you are using the MLflow open source mlflow server or mlflow ui commands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.

References More information about the vulnerability is available at https://nvd.nist.gov/vuln/detail/CVE-2023-1177.

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

MLflow allowed arbitrary files to be PUT onto the server.

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

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

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

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

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 basicauth.ini file. The file contains hard-coded default credentials. An attacker can leverage this vulnerability to bypass authentication and execute arbitrary code in the context of the administrator.

mlflow prior to 2.3.0 is vulnerable to path traversal due to a bypass of the fix for CVE-2023-1177.

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

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 in GitHub repository mlflow/mlflow prior to 2.9.2.

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 'islocaluri' function's failure to properly handle URIs with empty or 'file' schemes, leading to the misclassification of URIs as non-local. Attackers can exploit this by crafting malicious model versions with specially crafted 'source' parameters, enabling the reading of sensitive files within at least two directory levels from the server's root.

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

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.

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.

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.

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 due to unfiltered input.

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.

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.

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.

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.

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.

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.

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

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

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

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 deleteartifactmlflowartifacts handler and localfileuritopath function, allowing for the deletion of arbitrary directories on the server's filesystem. This vulnerability is due to an extra unquote operation in the deleteartifacts function of localartifactrepo.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.