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

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 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...

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...

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...

Software Vulnerability Detection Using a Lightweight Graph Neural Network

Large Language Models LLMs have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source availability, and variety of models, but have limited scalability due to extensive compute requirements. Using the natural graph relational structure o...

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...

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...

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...

CVE-2026-30308

In its design for automatic terminal command execution, HAI Build Code Generator offers two options: Execute safe commands and Execute all commands. The description for the former states that commands determined by the model to be safe will be automatically executed, whereas if the model judges a...

PT-2026-29099

Name of the Vulnerable Software and Affected Versions Node.js versions 25.x Description A flaw in the Node.js Permission Model’s network enforcement allows Unix Domain Socket UDS server operations to proceed without the necessary permission checks. All other network paths correctly enforce these...

nginx-ui's Unauthenticated MCP Endpoint Allows Remote Nginx Takeover

The nginx-ui MCP Model Context Protocol integration exposes two HTTP endpoints: /mcp and /mcpmessage. While /mcp requires both IP whitelisting and authentication AuthRequired middleware, the /mcpmessage endpoint only applies IP whitelisting - and the default IP whitelist is empty, which the...

EUVD-2026-16999

OpenClaw before 2026.3.11 contains a session sandbox escape vulnerability in the sessionstatus tool that allows sandboxed subagents to access parent or sibling session state. Attackers can supply arbitrary sessionKey values to read or modify session data outside their sandbox scope, including...