87 results · Feed
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High 7.0
Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf
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() MLflo…
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High 7.5
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 s…
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Critical 10.0CVE-2024-0520 Public exploit
Remote Code Execution due to Full Controlled File Write in mlflow/mlflow
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…
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Medium 5.4
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…
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High 8.8
MLFlow improper input validation
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8CVE-2024-37054 Public exploit
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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High 8.8
MLFlow unsafe deserialization
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.
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Medium 5.4
Improper Access Control in mlflow/mlflow
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…
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High 7.5
Path Traversal Bypass in mlflow/mlflow
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 …
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High 7.5CVE-2024-1483 Scanner template
Path Traversal Vulnerability in mlflow/mlflow
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 …
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High 7.5
Path Traversal Vulnerability in mlflow/mlflow
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` …
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High 7.5
Local File Read via Path Traversal in mlflow/mlflow
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 artifac…
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High 7.5
Path Traversal via Parameter Smuggling in mlflow/mlflow
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 unauthori…
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Critical 9.3
Local File Inclusion (LFI) via Scheme Confusion in mlflow/mlflow
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 emp…
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High 8.1
Path Traversal Vulnerability in mlflow/mlflow
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 a…
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High 7.5
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 fiel…
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High 7.5
Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe.
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.
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Critical 10.0CVE-2023-6977 Scanner template
Path Traversal: '\..\filename'
This vulnerability enables malicious users to read sensitive files on the server.
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High 8.8
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.
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Critical 9.8
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.
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High 8.6
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.
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Critical 9.0
Command Injection
with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.
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High 7.5
Path Traversal: '\..\filename' in mlflow/mlflow
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
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High 8.1CVE-2023-6831 Scanner template
Path Traversal: '\..\filename' in mlflow/mlflow
Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.
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Critical 9.6
Path Traversal in mlflow/mlflow
Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.
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Critical 10.0
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.
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Medium 6.5CVE-2023-6568 Scanner template
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 …
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High 7.5
Information exposure in MLflow
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.
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Critical 9.1
MLflow Authentication Bypass
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment.
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Critical 10.0
MLflow Arbitrary File Upload
MLflow allowed arbitrary files to be PUT onto the server.
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Critical 10.0
MLflow Arbitrary File Write
An attacker can overwrite any file on the server hosting MLflow without any authentication.
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High 8.8
OS Command Injection in mlflow/mlflow
OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.
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Critical 10.0
Absolute Path Traversal in mlflow/mlflow
Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.