Bitnami package
mlflow
pkg:bitnami/mlflow
Vulnerabilities (73)
| CVE | Sev | CVSS | KEV | Affected versions | Fixed in | Published | Description |
|---|---|---|---|---|---|---|---|
| CVE-2025-11201 | — | < 3.0.0 | 3.0.0 | Oct 29, 2025 | 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. | ||
| CVE-2025-52967 | Med | 5.8 | < 3.1.0 | 3.1.0 | Jun 23, 2025 | gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation. | |
| CVE-2025-0453 | — | >= 2.17.2, < 2.18.0 | 2.18.0 | Mar 20, 2025 | 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 appli | ||
| CVE-2025-1474 | — | < 2.19.0 | 2.19.0 | Mar 20, 2025 | 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 | ||
| CVE-2025-1473 | — | >= 2.17.0, < 2.20.1 | 2.20.1 | Mar 20, 2025 | 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. | ||
| CVE-2024-8859 | — | >= 2.15.1, < 2.16.0 | 2.16.0 | Mar 20, 2025 | 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 | ||
| CVE-2024-6838 | — | >= 2.13.2, < 2.14.0 | 2.14.0 | Mar 20, 2025 | 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 po | ||
| CVE-2024-27134 | — | < 2.16.0 | 2.16.0 | Nov 25, 2024 | 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. | ||
| CVE-2024-2928 | — | < 2.11.3 | 2.11.3 | Jun 6, 2024 | 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 '../ | ||
| CVE-2024-0520 | — | < 2.9.1 | 2.9.1 | Jun 6, 2024 | 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 sourc | ||
| CVE-2024-3099 | — | — | — | Jun 6, 2024 | 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 differen | ||
| CVE-2024-37061 | — | >= 1.11.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37060 | — | >= 1.27.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37059 | — | >= 0.5.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37058 | — | >= 2.5.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37057 | — | >= 2.0.0, <= 2.13.1 | — | Jun 4, 2024 | 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. | ||
| CVE-2024-37056 | — | >= 1.23.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37055 | — | >= 1.24.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37054 | — | >= 0.9.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. | ||
| CVE-2024-37053 | — | >= 1.1.0, < 2.13.2 | 2.13.2 | Jun 4, 2024 | 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. |
- CVE-2025-11201Oct 29, 2025affected < 3.0.0fixed 3.0.0
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.
- affected < 3.1.0fixed 3.1.0
gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation.
- CVE-2025-0453Mar 20, 2025affected >= 2.17.2, < 2.18.0fixed 2.18.0
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 appli
- CVE-2025-1474Mar 20, 2025affected < 2.19.0fixed 2.19.0
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
- CVE-2025-1473Mar 20, 2025affected >= 2.17.0, < 2.20.1fixed 2.20.1
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.
- CVE-2024-8859Mar 20, 2025affected >= 2.15.1, < 2.16.0fixed 2.16.0
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
- CVE-2024-6838Mar 20, 2025affected >= 2.13.2, < 2.14.0fixed 2.14.0
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 po
- CVE-2024-27134Nov 25, 2024affected < 2.16.0fixed 2.16.0
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.
- CVE-2024-2928Jun 6, 2024affected < 2.11.3fixed 2.11.3
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 '../
- CVE-2024-0520Jun 6, 2024affected < 2.9.1fixed 2.9.1
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 sourc
- CVE-2024-3099Jun 6, 2024
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 differen
- CVE-2024-37061Jun 4, 2024affected >= 1.11.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37060Jun 4, 2024affected >= 1.27.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37059Jun 4, 2024affected >= 0.5.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37058Jun 4, 2024affected >= 2.5.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37057Jun 4, 2024affected >= 2.0.0, <= 2.13.1
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.
- CVE-2024-37056Jun 4, 2024affected >= 1.23.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37055Jun 4, 2024affected >= 1.24.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37054Jun 4, 2024affected >= 0.9.0, < 2.13.2fixed 2.13.2
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.
- CVE-2024-37053Jun 4, 2024affected >= 1.1.0, < 2.13.2fixed 2.13.2
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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