agentforge-security

Design multi-layer security for autonomous agents with Starlark policies.

Updated Apr 8, 2026

Search Tags:#security #llm #compliance #guardian #policy #sandbox #approval

Ready to run

npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-security

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentforge-security
Source: https://github.com/Kingxiao/agentforge/tree/main/agentforge-security
Command: npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-security

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Agent autonomy without robust security layers can lead to unsafe executions; this Skill provides a comprehensive security framework to design, enforce, and verify multi-layer protections for AI agents.

Core Features & Use Cases

6-layer security model spanning Tool Permissions, Input Validation, Command Policy Engine, Path Restrictions, OS Sandbox, and Container Isolation.
Integrated Starlark policy engine with deterministic evaluation plus Guardian AI risk assessment and multi-agent approval flows for enterprise governance.
Suitable for production-grade deployments requiring compliance, risk management, and auditability across complex toolchains.

Quick Start

Install and configure the 6-layer security model in your agent runtime and start a session to enforce policy-driven execution.

Frequently Asked Questions about agentforge-security

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I secure autonomous AI agents against prompt injection and data exfiltration?▼

To secure autonomous AI agents against prompt injection and data exfiltration, implement a 6-layer security model covering tool permissions, input validation, command policy, path restrictions, OS sandbox, and container isolation to enforce deterministic policy evaluation.

What is the best way to enforce compliance and risk management for LLM agent execution?▼

The best way to enforce compliance for LLM agent execution is integrating a Starlark policy engine for deterministic evaluation alongside a Guardian AI risk assessment layer, enabling multi-agent approval flows to govern complex toolchains.

How do I set up a multi-layer security defense for production-grade agent runtimes?▼

To set up multi-layer security defense for production-grade agent runtimes, configure tool permissions, input validation, command policy engine, path restrictions, OS sandbox, and container isolation, then start a session to enforce policy-driven execution.

Does AgentForge Security support deterministic policy evaluation for enterprise governance?▼

Yes, AgentForge Security supports deterministic policy evaluation for enterprise governance through an integrated Starlark policy engine, combined with Guardian AI risk assessment and multi-agent approval flows to ensure auditability.

Can I use sandbox and container isolation to prevent unsafe command execution in autonomous agents?▼

Yes, you can use OS sandbox and container isolation layers to prevent unsafe command execution in autonomous agents, restricting unauthorized paths and enforcing command policy to block policy violations and data exfiltration.

When do I need a command policy engine for AI agent security?▼

You need a command policy engine for AI agent security when deploying autonomous agents in production environments requiring strict compliance, risk management, and auditability across complex toolchains to prevent unsafe executions.