autonomize-model-sdk (1.0.4), databricks-genai (>=1.0.8, <=1.0.13) +1 more potentially affected by CVE-2025-15379 via mlflow (2.14.2)

mlflow PYPI version 2.14.2 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - autonomize-model-sdk 1.0.4 - databricks-genai =1.0.8, =1.0.33, =1.0.37 Source cves: CVE-2025-15379 Source advisory: OSV:GHSA-R23Q-823P-VMF7...

mlflow-cratedb (2.13.0), mlflowcli (0.0.2) +1 more potentially affected by CVE-2025-15379 via mlflow (2.13.0)

mlflow PYPI version 2.13.0 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - mlflow-cratedb 2.13.0 - mlflowcli 0.0.2 - model-controller 1.0.0 Source cves: CVE-2025-15379 Source advisory: OSV:GHSA-R23Q-823P-VMF7...

EUVD-2025-209121

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

GHSA-R23Q-823P-VMF7 MLflow Command Injection vulnerability

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

MLflow Command Injection vulnerability

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

Arbitrary Command Injection

Overview Affected versions of this package are vulnerable to Arbitrary Command Injection in the installmodeldependenciestoenv function. An attacker can execute arbitrary commands by supplying a crafted model artifact containing malicious dependency specifications in the pythonenv.yaml file, which...

Arbitrary Command Injection

Overview mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Arbitrary Command Injection in the installmodeldependenciestoenv...

CVE-2025-15379

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

CVE-2025-15379 Command Injection in mlflow/mlflow

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

CVE-2025-15379

Summary: CVE-2025-15379 affects MLflow (model serving container initialization). In the function _install_model_dependencies_to_env(), when deploying with env_manager=LOCAL, dependency specs from the model artifact's python_env.yaml are interpolated into a shell command without sanitization, enab...

CVE-2025-15379

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

CVE-2025-15379 Command Injection in mlflow/mlflow

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

CVE-2025-15379 Command Injection in mlflow/mlflow

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

mlflow-cratedb (2.13.0), mlflowcli (0.0.2) +1 more potentially affected by CVE-2025-15036 via mlflow (2.13.0)

mlflow PYPI version 2.13.0 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - mlflow-cratedb 2.13.0 - mlflowcli 0.0.2 - model-controller 1.0.0 Source cves: CVE-2025-15036 Source advisory: OSV:GHSA-VHCX-3PQ2-4FVC...

emb-model (>=0.1.0, <=0.2.9), mlflow-cratedb (2.11.3) +2 more potentially affected by CVE-2025-15036 via mlflow (2.11.3)

mlflow PYPI version 2.11.3 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - emb-model =0.1.0, =0.0.3.dev0, =0.0.15 - smartdeploymiguel 0.1.1 Source cves: CVE-2025-15036 Source advisory: OSV:GHSA-VHCX-3PQ2-4FVC...

autonomize-model-sdk (1.0.4), databricks-genai (>=1.0.8, <=1.0.13) +1 more potentially affected by CVE-2025-15036 via mlflow (2.14.2)

mlflow PYPI version 2.14.2 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - autonomize-model-sdk 1.0.4 - databricks-genai =1.0.8, =1.0.33, =1.0.37 Source cves: CVE-2025-15036 Source advisory: OSV:GHSA-VHCX-3PQ2-4FVC...

autonomize-model-sdk (>=1.0.6, <=1.1.21), experiment-goodies (>=0.1.1, <=0.1.2) +1 more potentially affected by CVE-2025-15036 via mlflow (2.20.1)

mlflow PYPI version 2.20.1 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - autonomize-model-sdk =1.0.6, =0.1.1, =0.1.2 - terratorch 0.99.8 Source cves: CVE-2025-15036 Source advisory: OSV:GHSA-VHCX-3PQ2-4FVC...

Why Aggregate Accuracy Is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems

Facial recognition systems are increasingly deployed in law enforcement and security contexts, where algorithmic decisions can carry significant societal consequences. Despite high reported accuracy, growing evidence demonstrates that such systems often exhibit uneven performance across demograph...

Awesome LLM Apps has security vulnerabilities

Awesome LLM Apps is a collection of large language model applications personally developed by Shubham Saboo. Awesome LLM Apps contains security vulnerabilities, which stem from improper isolation of session-specific environment variables, potentially leading to cross-session information leaks...

Safeguarding LLMs against Misuse and AI-Driven Malware Using Steganographic Canaries

AI-powered malware increasingly exploits cloud-hosted generative-AI services and large language models LLMs as analysis engines for reconnaissance and code generation. Simultaneously, enterprise uploads expose sensitive documents to third-party AI vendors. Both threats converge at the AI service...