Hands-on workshop
Designing Safer AI Agents
Secure-by-Design Patterns for AI Systems
AI systems don't fail like traditional software — they fail silently, follow the wrong authority, and can be steered into leaking data or taking unintended actions. This 4-hour hands-on workshop teaches you how modern AI vulnerabilities actually show up in deployed LLM features by attacking and defending a sandboxed car dealership chatbot that's connected to an internal database. Then you will pivot to real-world data exfiltration patterns via direct and indirect prompt injection (including untrusted content in RAG-like workflows).
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Prompts
Workshop Materials
Slides & Notes
Prompt Injection FundamentalsView Slides
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About the Instructor
Pavan Reddy
Pavan Reddy is an AI security researcher and builder, and the founder of QBTrain — a hands-on platform for learning AI security and AppSec. He started inside AI (adversarial ML, model internals) and now focuses on breaking and securing real LLM and agentic systems: prompt injection, data exfiltration, and the systemic weaknesses of foundation models. He has published at AAAI, ACM, NeurIPS, and FLAIRS, and teaches a small set of signature workshops across BSides, OWASP, and academic venues. As Principal Developer at Automata, he owns a security product end to end.