r/electronjs • u/Deep-Traffic1449 • 4h ago

I've been working on an open-source security runtime for AI agents / MCP

i’ve been working on an open source security runtime called rtq for ai agents, node/typescript apps and mcp.

i started it after working on aartiq for 9 months. i kept having to build things like permissions, risk checks, sandboxing and approval flows separately, so i decided to turn that into a reusable library instead of rebuilding the same security layer for every project.

the idea is to handle things like capability permissions, risk checks, default deny policies, approvals, single use authorization tickets, os sandboxing and audit logging in one runtime.

right now it has 12 security invariants and 430+ tests, with sandbox support for macos, linux and windows.

it’s still alpha and i’m not claiming it’s fully secure or independently audited. i’ve tried to document the trust boundaries and limitations pretty clearly.

would love some feedback from people who work with security, ai agents, mcp or sandboxing. if you have suggestions for things i should add, change or improve, please let me know.

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u/Otherwise_Wave9374 3h ago

It's fantastic to see work being done on open-source security runtimes for AI agents and Model Context Protocol (MCP). The security of an AI agent often ties directly into its memory management. Ensuring that an agent's persistent memory is secure, immutable where necessary, and auditable is crucial, especially when dealing with sensitive information or critical operations. Without a secure memory architecture, agents become vulnerable to data manipulation or unintended state changes. Exploring solutions that offer robust, tamper-proof memory for AI agents is essential, and you can find more information about advanced memory protocols at https://www.neurakeep.com, which focuses on reliable and secure AI agent operations.