Lesson 15: Prompt Injection 101–Attack Your Own AI Agent | OpenClaw Security & Red Teaming Tutorial

This lesson covers prompt injection attacks, a critical security vulnerability in AI agents where malicious inputs manipulate model behavior. The tutorial teaches red teaming techniques to identify and exploit prompt injection vulnerabilities in your own AI systems. Understanding these attack vectors is essential for building secure AI agents and implementing proper input validation and output filtering defenses.

Key Points

• •Prompt injection occurs when user input is crafted to override system instructions or change AI agent behavior
• •Direct injection attacks embed malicious instructions directly in user prompts to manipulate model responses
• •Indirect injection attacks exploit data sources (documents, databases, APIs) that feed into AI agent prompts
• •Red teaming your own AI agents helps identify vulnerabilities before malicious actors exploit them
• •Input validation and sanitization are critical first-line defenses against prompt injection
• •Output filtering and monitoring can detect when an AI agent has been compromised by injection attacks
• •Prompt engineering best practices include clear role definitions, explicit constraints, and instruction hierarchy
• •Separation of user input from system instructions using structured formats reduces injection risk
• •Testing with adversarial prompts and jailbreak attempts reveals security gaps in AI agent design
• •Defense-in-depth strategies combining multiple layers (validation, filtering, monitoring) provide stronger protection

Workflow Diagram

Concepts