How to Harden AI Agents Against Prompt Injections & Tool Poisoning (Open-Source Guide)
Building autonomous AI agents and tool-calling systems is exciting, but securing them is a major challenge. If you connect an LLM to external APIs or databases, an attacker who manipulates the model via prompt injection can hijack tools or extract internal prompts. In this guide, we'll walk through
Building autonomous AI agents and tool-calling systems is exciting, but securing them is a major challenge.
If you connect an LLM to external APIs or databases, an attacker who manipulates the model via prompt injection can hijack tools or extract internal prompts.
In this guide, we'll walk through the architectural defense layers needed to protect agentic apps, along with an open-source CI/CD linter.
Never feed untrusted user inputs directly into an LLM context. Modern LLM security relies on strict XML boundary encapsulation: