Provides a framework to benchmark, evaluate, and inject real-time guardrails across competing MCP servers, CLIs, and AI agent tools. It enables recursive self-improvement by mining failures and synthesizing weakest valid guardrails.
An intentionally vulnerable MCP server for security training, enabling users to practice attacking and defending AI agents through realistic scenarios.
A sandbox-first MCP server that enables AI agents to safely perform reverse-engineering tasks in fully isolated Docker containers, featuring zero-trust security, declarative YAML-based skills, and white-hat ethical safeguards.
Security co-pilot for AI agents. Scans for vulnerabilities like prompt injection, infinite loops, and token bombing in AI Agents, audits MCP servers, verifies AGENTS.md governance, and generates EU AI Act compliance reports.
An educational project that deliberately implements vulnerable MCP servers to demonstrate various security risks like prompt injection, tool poisoning, and code execution for training security researchers and AI safety professionals.
