Lfprojects vulnerabilities, page 3

114 vulnerabilities

Known vulnerabilities in Lfprojects products, newest first, with CVSS severity, EPSS, KEV listings and ransomware use, across 18 products.

8.8CVE-2024-37054chainguardmlflow+6
EPSS score
0.70%
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.8.8CVE-2024-37059chainguardmlflow+6
EPSS score
0.62%
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.7CVE-2024-27134lfprojectsmlflow+1
EPSS score
0.12%
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.9.6CVE-2024-27133lfprojectsmlflow+1
EPSS score
0.66%
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.9.6CVE-2024-27132lfprojectsmlflow+1
EPSS score
0.88%
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.7.5CVE-2024-27093github.com/stacklokminder+2
EPSS score
0.55%
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.53868a8.7.1CVE-2024-27916github.com/stacklokminder+2
EPSS score
0.67%
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.2.8CVE-2024-22194cyber-domain-ontologycdo-utility-local-uuid+4
EPSS score
0.41%
cdo-local-uuid project provides a specialized UUID-generating function that can, on user request, cause a program to generate deterministic UUIDs. An information leakage vulnerability is present in `cdo-local-uuid` at version `0.4.0`, and in `case-utils` in unpatched versions (matching the pattern `0.x.0`) at and since `0.5.0`, before `0.15.0`. The vulnerability stems from a Python function, `cdo_local_uuid.local_uuid()`, and its original implementation `case_utils.local_uuid()`.5.3CVE-2024-6838lfprojectsmlflow+2
EPSS score
0.66%
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 potential denial of service. Additionally, there is no character limit in the `artifact_location` parameter while creating the experiment.7.5CVE-2024-8859lfprojectsmlflow+2
EPSS score
2.77%
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 checked, while parts such as query and parameters are not handled. The vulnerability is triggered if the user has configured the dbfs service, and during usage, the service is mounted to a local directory.9.3CVE-2024-3573lfprojectsmlflow+2
EPSS score
0.73%
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.7.5CVE-2024-3848lfprojectsmlflow+2
EPSS score
43.28%
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.5.4CVE-2024-3099lfprojectsmlflow+2
EPSS score
0.44%
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.7.5CVE-2026-21863alpinevalkey+12
EPSS score
0.79%
Valkey is a distributed key-value database. Prior to versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12, a malicious actor with access to the Valkey clusterbus port can send an invalid packet that may cause an out bound read, which might result in the system crashing. The Valkey clusterbus packet processing code does not validate that a clusterbus ping extension packet is located within buffer of the clusterbus packet before attempting to read it. Versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12 fix the issue. As an additional mitigation, don't expose the cluster bus connection directly to end users, and protect the connection with its own network ACLs.7.5CVE-2026-21864lfprojectsvalkey-bloom+1
EPSS score
0.27%
Valkey-Bloom is a Rust based Valkey module which brings a Bloom Filter (Module) data type into the Valkey distributed key-value database. Prior to commit a68614b6e3845777d383b3a513cedcc08b3b7ccd, a specially crafted `RESTORE` command can cause Valkey to hit an assertion, causes the server to shutdown. Valkey modules are required to handle errors in RDB parsing by using `VALKEYMODULE_OPTIONS_HANDLE_IO_ERRORS` flag. If this flag is not set, errors encountered during parsing result in a system assertion which shuts down the system. Even though the Valkey-bloom module correctly handled the parsing, it did not originally set the flag. Commit a68614b6e3845777d383b3a513cedcc08b3b7ccd contains a patch. One may mitigate this defect by disabling the `RESTORE` command if it is unused by one's application.7.1CVE-2026-40090github.com/defenseunicornszarf+3
EPSS score
0.39%
Zarf is an Airgap Native Packager Manager for Kubernetes. Versions 0.23.0 through 0.74.1 contain an arbitrary file write vulnerability in the zarf package inspect sbom and zarf package inspect documentation subcommands. These subcommands output file paths are constructed by joining a user-controlled output directory with the package's Metadata.Name field read directly from the untrusted package's zarf.yaml manifest. Although Metadata.Name is validated against a regex on package creation, an attacker can unarchive a package to modify the Metadata.Name field to contain path traversal sequences such as ../../etc/cron.d/malicious or absolute paths like /home/user/.ssh/authorized_keys, along with the corresponding files inside SBOMS.tar. This allows writing attacker-controlled content to arbitrary filesystem locations within the permissions of the user running the inspect command. This issue has been fixed in version 0.74.2.7.5CVE-2026-2614lfprojectsmlflow+2
EPSS score
3.21%
A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.6.5CVE-2026-2734lfprojectsmlflow+2
EPSS score
0.50%
In mlflow/mlflow versions up to 3.9.0, the `SearchModelVersions` REST API endpoint and the `mlflowSearchModelVersions` GraphQL query lack proper per-model authorization checks when basic authentication is enabled. This allows any authenticated user to enumerate all model versions across all registered models, regardless of their permission level. The issue arises due to the absence of `SearchModelVersions` in the `BEFORE_REQUEST_VALIDATORS` and `AFTER_REQUEST_HANDLERS` for the REST API, and its omission from `GraphQLAuthorizationMiddleware.PROTECTED_FIELDS` for GraphQL. This vulnerability can expose sensitive information such as model names, version descriptions, source URIs, tags, and other metadata, potentially revealing proprietary or confidential details in multi-tenant environments. The issue is resolved in version 3.10.0.7.1CVE-2026-2393lfprojectsmlflow+2
EPSS score
0.29%
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function in `mlflow/server/handlers.py` accepts a user-controlled `url` parameter without validation, and the `_send_webhook_request()` function in `mlflow/webhooks/delivery.py` sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.9CVE-2026-2651chainguardmlflow+4
EPSS score
0.54%
A vulnerability in MLflow versions <=3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when the `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded. The issue is resolved in version 3.10.0.