🛡️ Defense Architecture for AI Agents: How to Secure Your LLMs Against Prompt Injection, Tool-Poisoning, and Fugitivity.
🛡️ Arquitectura de Defensa para Agentes de IA: Cómo asegurar tus LLMs contra Prompt Injection, Tool-Poisoning y Fugitividad. El ecosistema actual de agentes autónomos y servidores MCP (Model Context Protocol) es brillante, pero operativamente es una pesadilla de seguridad. Estamos construyendo sistemas que ejecutan código, acceden a bases de datos y toman decisiones críticas basándose en salidas…
An expert has developed a four-layer defense framework to protect autonomous AI agents from security threats such as prompt injection, tool-poisoning, and data breaches. The framework, which is open-source and CPU-only, includes tools such as hermes-shield, vision-injection-guard, and agent-shield-runtime to sanitize input, validate physical interactions, and monitor runtime activity.
The framework also includes measures to defend the ecosystem of tools and data, such as mcp-schema-sentinel and skill-auditor. The expert argues that current autonomous agent systems are vulnerable to security threats and that this framework can provide a robust defense. The framework is available on GitHub at https://github.com/amurlaniakea.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
