15124 matches found
vLLM Denial of Service via the best_of parameter
A vulnerability was found in the ilab model serve component, where improper handling of the bestof parameter in the vllm JSON web API can lead to a Denial of Service DoS. The API used for LLM-based sentence or chat completion accepts a bestof parameter to return the best completion from several...
PYSEC-2026-2024 vLLM denial of service vulnerability
A flaw was found in the vLLM library. A completions API request with an empty prompt will crash the vLLM API server, resulting in a denial of service...
PYSEC-2026-1928 Skops unsafe deserialization
Deserialization of untrusted data can occur in versions 0.6 or newer of the skops python library, enabling a maliciously crafted model to run arbitrary code on an end user's system when loaded...
Skops unsafe deserialization
Deserialization of untrusted data can occur in versions 0.6 or newer of the skops python library, enabling a maliciously crafted model to run arbitrary code on an end user's system when loaded...
Undefined Behavior in 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 will open a different model each time...
PYSEC-2026-1645 Undefined Behavior in 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 will open a different model each time...
PYSEC-2026-1654 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...
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...
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...
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...
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...
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...
PYSEC-2026-1643 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...
PYSEC-2026-1644 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...
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...
PYSEC-2026-1651 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...
Wagtail has permission check bypass when editing a model with per-field restrictions through `wagtail.contrib.settings` or `ModelViewSet`
ImpactIf a model has been made available for editing through the wagtail.contrib.settings module or ModelViewSet, and the permission argument on FieldPanel has been used to further restrict access to one or more fields of the model, a user with edit permission over the model but not the specific...
PYSEC-2026-1653 mlflow vulnerable to Path Traversal
A path traversal vulnerability exists in the createmodelversion 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 parameter that bypasses the...
mlflow vulnerable to Path Traversal
A path traversal vulnerability exists in the createmodelversion 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 parameter that bypasses the...