Large Language Models (LLMs) are reshaping how we build applications—but with great power comes great vulnerability. Prompt injection attacks exploit the very thing that makes LLMs so useful: their ability to follow natural language instructions. The result? Malicious prompts that can hijack model behavior, often in subtle and dangerous ways.
While prompt injection is now widely recognized, the defenses being deployed across the industry often fall short. Why? Because what works in one context—one model, one use case—can completely fail in another. In this talk, we’ll go beyond just classifying attack types to focus on what really matters: how to build prompt defenses that actually work.
We’ll dig into practical, layered defense strategies—like prompt hardening, input/output validation, and system prompt design—while highlighting why secure prompting must be tailored to your model architecture, application flow, and risk surface. From SLMs to multi-modal inputs, we’ll show how “one prompt to rule them all” just doesn’t exist.
You’ll also get an overview of emerging tools for stress-testing and validating your prompt security, helping you move from reactive patching to proactive defense. If you're building with LLMs, it's time to think beyond generic guardrails and start securing prompts like it actually matters—because it does.