Filtered by vendor Lfprojects
Subscriptions
Total
58 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2024-27134 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7 High |
| 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-2024-1483 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 7.5 High |
| 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. | ||||
| CVE-2024-37061 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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-2024-37052 | 1 Lfprojects | 1 Mlflow | 2025-02-03 | 8.8 High |
| 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-2023-2356 | 1 Lfprojects | 1 Mlflow | 2025-01-30 | 7.5 High |
| Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1. | ||||
| CVE-2023-30172 | 1 Lfprojects | 1 Mlflow | 2025-01-27 | 7.5 High |
| 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. | ||||
| CVE-2024-3848 | 1 Lfprojects | 1 Mlflow | 2025-01-24 | 7.5 High |
| 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 result, an attacker can read arbitrary files, including sensitive information such as SSH and cloud keys, by exploiting the way the application converts the URL into a filesystem path. The issue stems from insufficient validation of the fragment portion of the URL, leading to arbitrary file read through path traversal. | ||||
| CVE-2023-2780 | 1 Lfprojects | 1 Mlflow | 2025-01-22 | 9.8 Critical |
| Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1. | ||||
| CVE-2024-27132 | 1 Lfprojects | 1 Mlflow | 2025-01-22 | 7.5 High |
| 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. | ||||
| CVE-2024-27133 | 1 Lfprojects | 1 Mlflow | 2025-01-22 | 7.5 High |
| 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. | ||||
| CVE-2024-3099 | 1 Lfprojects | 1 Mlflow | 2024-11-21 | 5.4 Medium |
| 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 name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts. | ||||
| CVE-2024-2928 | 1 Lfprojects | 1 Mlflow | 2024-11-21 | 7.5 High |
| 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 a bypass to a previous patch that only addressed similar manipulation within the URI's query string, highlighting the need for comprehensive validation of all parts of a URI to prevent LFI attacks. | ||||