firefox: thunderbird: Sandbox escape due to incorrect boundary conditions, integer overflow in the XPCOM component

A flaw was found in Firefox and Thunderbird. The Mozilla Foundation's Security Advisory describes the following issue: Sandbox escape due to incorrect boundary conditions, integer overflow in the XPCOM component...

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...

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...

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...

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

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...

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...

PT-2026-28801

Name of the Vulnerable Software and Affected Versions MLflow versions 3.8.0 through 3.8.1 Description A command injection issue exists in MLflow’s model serving container initialization code, specifically within the install model dependencies to env function. When deploying a model with env...

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...

Label-Efficient Training Updates for Malware Detection over Time

Machine Learning ML-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings, where both legitimate and malicious software evolve. This distribution drift causes models...

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...

CVE-2026-29872

A cross-session information disclosure vulnerability exists in the awesome-llm-apps project in commit e46690f99c3f08be80a9877fab52acacf7ab8251 2026-01-19. The affected Streamlit-based GitHub MCP Agent stores user-supplied API tokens in process-wide environment variables using os.environ without...

PT-2026-29119

Name of the Vulnerable Software and Affected Versions SakaDev affected versions not specified Description SakaDev’s automatic terminal command execution feature, designed with ‘safe’ and ‘all commands’ options, is prone to prompt injection attacks. The system aims to automatically execute command...

Awesome LLM Apps 安全漏洞

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...

PT-2026-29104

Name of the Vulnerable Software and Affected Versions Docker Model Runner versions prior to 1.1.25 Docker Desktop versions prior to 4.67.0 Description The software contains a Server-Side Request Forgery SSRF issue within the OCI registry token exchange process. When retrieving a model, the softwa...