Vulnerable AI Systems: Real Data, Responsible Design

29% of attacks bypass the security filters of the most widely used LLMs in production. It's not a bug. It's the nature of the system. LLMs are stochastic processes trained on human language—the most flexible, ambiguous, and manipulable medium that exists. This talk presents the results of llm-break-bench: 3,360 adversarial tests on GPT-4o, Claude, Gemini, Grok, and DeepSeek using MLCommons AI Safety v0.5 and OWASP LLM Top 10 as standards. The smartest model in the benchmark is 5 times more vulnerable than the cheapest. The data connects to real use cases where LLMs are in production: RAGs, chatbots, agents, code assistants. The closing is actionable: 5 design pillars for AI systems that don't depend on the model for their own security, with real code from NVIDIA NeMo Guardrails and Meta LlamaFirewall.