Filtered by vendor Lfprojects
Subscribe
Total
113 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2024-4263 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 5.4 MEDIUM |
| 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 a low privilege user to delete a directory inside an artifact using a DELETE request, despite the official documentation stating that users with EDIT permission can only read and update artifacts, not delete them. | |||||
| CVE-2024-3848 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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-2024-3573 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 9.3 CRITICAL |
| 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 files within at least two directory levels from the server's root. | |||||
| CVE-2024-3099 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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-2024-37061 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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 | 2026-06-17 | N/A | 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-2024-2928 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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. | |||||
| CVE-2024-27916 | 1 Lfprojects | 1 Minder | 2026-06-17 | N/A | 7.1 HIGH |
| Minder is a software supply chain security platform. Prior to version 0.0.33, a Minder user can use the endpoints `GetRepositoryByName`, `DeleteRepositoryByName`, and `GetArtifactByName` to access any repository in the database, irrespective of who owns the repo and any permissions present. The database query checks by repo owner, repo name and provider name (which is always `github`). These query values are not distinct for the particular user - as long as the user has valid credentials and a provider, they can set the repo owner/name to any value they want and the server will return information on this repo. Version 0.0.33 contains a patch for this issue. | |||||
| CVE-2024-27134 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 7.0 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-2024-27133 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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-2024-27132 | 1 Lfprojects | 1 Mlflow | 2026-06-17 | N/A | 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-2024-27093 | 1 Lfprojects | 1 Minder | 2026-06-17 | N/A | 4.6 MEDIUM |
| Minder is a Software Supply Chain Security Platform. In version 0.0.31 and earlier, it is possible for an attacker to register a repository with a invalid or differing upstream ID, which causes Minder to report the repository as registered, but not remediate any future changes which conflict with policy (because the webhooks for the repo do not match any known repository in the database). When attempting to register a repo with a different repo ID, the registered provider must have admin on the named repo, or a 404 error will result. Similarly, if the stored provider token does not have repo access, then the remediations will not apply successfully. Lastly, it appears that reconciliation actions do not execute against repos with this type of mismatch. This appears to primarily be a potential denial-of-service vulnerability. This vulnerability is patched in version 0.20240226.1425+ref.53868a8. | |||||