Lilly Tech Systems
Prompt Injection & AI Security
Master the security landscape of LLM-powered applications. Learn about jailbreaks, indirect injection, data exfiltration, defense layers, input sanitization, output filtering, and how to build resilient AI systems.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is prompt injection? Why LLMs are vulnerable, the threat landscape, and real-world incidents.
2. Attack Types
Direct injection, indirect injection, jailbreaks, data exfiltration, privilege escalation, and multi-step attacks.
3. Defense Strategies
Input sanitization, output filtering, privilege separation, defense in depth, and prompt hardening techniques.
4. Testing
Adversarial testing methodologies, fuzzing LLM inputs, automated vulnerability scanning, and evaluation metrics.
5. Tools
Security scanning tools, guardrail frameworks, monitoring solutions, and open-source defense libraries.
6. Best Practices
Security-first architecture, defense checklists, incident response, compliance, and staying ahead of evolving threats.
What You'll Learn
By the end of this course, you'll be able to:
Identify Attack Vectors
Recognize and classify prompt injection attacks including jailbreaks, indirect injection, and data exfiltration.
Build Defense Layers
Implement multi-layer defenses using input sanitization, output filtering, and privilege separation.
Test for Vulnerabilities
Conduct systematic security testing of LLM applications using adversarial testing frameworks.
Use Security Tools
Deploy and configure industry-standard tools for monitoring and protecting AI systems in production.