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GitHub MCP Gateway is a secure gateway for MCP servers, enabling controlled access to MCP servers for AI agents in sandboxed environments. It features security capabilities such as WASM-based DIFC protection, integrity filtering, and proxy mode.
Section 01
GitHub MCP Gateway is a secure gateway for MCP servers, designed to enable AI agents to access external resources (like GitHub) safely in controlled environments. Key features include WASM-based DIFC protection, integrity filtering, proxy mode, and containerized deployment, addressing the challenge of managing AI agents' access to external tools securely.
Section 02
MCP (Model Context Protocol) is an open protocol by Anthropic for standardizing AI model interactions with external data/tools. GitHub MCP Gateway serves as a gateway implementation for the GitHub Agentic Workflows project, aiming to balance MCP's flexibility with strict security controls to prevent unauthorized access or data leaks. It uses containerized deployment (Docker) to proxy AI agents' MCP requests from sandboxes and apply preset security policies.
Section 03
The gateway follows a 'security-first' design, with core components:
/mcp/{serverID}) and Unified Mode (single /mcp endpoint).Authorization header).Section 04
The gateway uses two main policies:
Section 05
Beyond MCP gateway, it supports HTTP forward proxy mode (awmg proxy) to intercept GitHub API requests (e.g., from gh CLI). This mode maps ~25 REST URL patterns and GraphQL queries, applying the same 6-stage DIFC filtering as the MCP gateway. Dual-mode design unifies security policies across MCP and traditional HTTP requests.
Section 06
Deployment steps:
docker pull ghcr.io/github/gh-aw-mcpg:latestconfig.json (gateway params, MCP server configs).MCP_GATEWAY_PORT (listen port), MCP_GATEWAY_API_KEY (auth key), MCP_GATEWAY_WASM_GUARDS_DIR (WASM guards directory).Section 07
Main use cases:
Section 08
GitHub MCP Gateway is an important exploration in AI infrastructure security, combining access control with MCP flexibility. Its WASM DIFC, fine-grained policies, and dual-mode design address production needs. As AI agents take on more complex tasks, such secure gateways will become essential. It sets a paradigm for balancing openness and control in AI agent workflows.
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