Open actionable advisories and recorded vendor fixes. This public package view has no inventory attached and cannot establish whether your deployment is affected. A vendor fix never resolves your finding; verify your installed version and the source guidance.
Open advisories
68 actionable advisories, newest first. Withdrawn advisories and news are excluded. Coverage is partial.
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
Recommended action: Upgrade to 2.9.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
Recommended action: Upgrade to 2.9.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.
Recommended action: Upgrade to 2.9.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.
Recommended action: Upgrade to 2.9.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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.
Recommended action: Upgrade to 2.9.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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.
Recommended action: Upgrade to 2.9.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirement.
Recommended action: Upgrade to 2.8.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
The mlflow web server includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. As this vulnerability allows to write / overwrite any file on the file system, it gives a lot of ways to archive code execution (like overwriting /home/<user/.bashrc). A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
Recommended action: Upgrade to 2.9.2 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
MLflow allowed arbitrary files to be PUT onto the server.
Recommended action: Upgrade to 2.8.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.
Recommended action: Upgrade to 2.6.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.
Recommended action: Upgrade to 2.5.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
mlflow prior to 2.3.0 is vulnerable to path traversal due to a bypass of the fix for CVE-2023-1177.
Recommended action: Upgrade to 2.3.0 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
A directory traversal vulnerability in the /get-artifact API method of the mlflow platform prior to v2.0.0 allows attackers to read arbitrary files on the server via the path parameter.
Recommended action: Upgrade to 2.0.0rc0 (pre-release) or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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 2.3.1 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 is very similar to https://nvd.nist.gov/vuln/detail/CVE-2023-1177, and a separate CVE will be published and updated here shortly. Patches This vulnerability has been patched in MLflow 2.3.1, which was released to…
Recommended action: Upgrade to 2.3.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.
Recommended action: Upgrade to 2.3.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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 existence check 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-2023-1176 enables an actor to check the existence of arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations…
Recommended action: Upgrade to 2.2.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
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…
Recommended action: Upgrade to 2.2.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.
mlflow prior to 1.23.1 contains an insecure temporary file. The insecure function tempfile.mktemp() is deprecated and mkstemp() should be used instead.
Recommended action: Upgrade to 1.23.1 or later, then redeploy and verify.A vendor fix does not resolve your finding until you verify it in your own systems.