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.
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 …
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 …
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 …
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 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 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 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with.
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 1.1.0 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.
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 be used to insert a path into the fragment, effectively skipping validation. This allows an attacker to construct a URL that, when processed, ignores the protocol scheme and uses the provided path for filesystem access. As a …
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 for DELETE requests by users with EDIT permissions, allowing them to perform unauthorized deletions of artifacts. The vulnerability specifically affects the handling of artifact deletions within the application, as demonstrated by the ability of …
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 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 …
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 crafting a source parameter that bypasses the _validate_non_local_source_contains_relative_paths(source) function's checks, allowing for arbitrary file read access on the server. The issue arises from the handling of unquoted URL characters and the subsequent misuse of the original source value for model version …
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_artifacts handler and local_file_uri_to_path function, allowing for the deletion of arbitrary directories on the server's filesystem. This vulnerability is due to an extra unquote operation in the delete_artifacts function of local_artifact_repo.py, which fails to properly sanitize user-supplied paths. The issue is present …
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 URL to gain unauthorized access to files or directories. This vulnerability allows for arbitrary data smuggling into the 'params' part of the URL, enabling attacks similar to those described in previous reports but utilizing the …
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 component # in the artifact location URI to read arbitrary files on the server in the context of the server's process. This issue is similar to CVE-2023-6909 but utilizes a different component of the URI to achieve the same effect.
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, using a local URI with '#' instead of '?', an attacker can traverse the server's directory structure. The issue occurs due to insufficient validation of user-supplied input in the server's handlers.
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.
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 template variables.
This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.
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.
This vulnerability enables malicious users to read sensitive files on the server.
A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.
Path Traversal: '..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
Path Traversal: '..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.
Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.
Cross-site Scripting (XSS) - Reflected in GitHub repository mlflow/mlflow prior to 2.9.0.
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.
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment.
MLflow allowed arbitrary files to be PUT onto the server.
An attacker can overwrite any file on the server hosting MLflow without any authentication.
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.
mlflow prior to 2.3.0 is vulnerable to path traversal due to a bypass of the fix for CVE-2023-1177.
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.
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 …
Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.
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 …
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 …
mlflow prior to 1.23.1 contains an insecure temporary file. The insecure function tempfile.mktemp() is deprecated and mkstemp() should be used instead.