Organizations deploying LLM-integrated applications face failure at three control points: system prompt integrity, output handling, and runtime visibility gaps. The problem arises when system prompts are not separated from user input, creating direct prompt injection exposure, and when AI-generated content is routed into downstream systems without output validation, enabling code execution and data exfiltration. Additionally, AI applications are monitored with assumptions for traditional web applications, missing AI-specific attack patterns. This failure pattern creates compounding risk: prompt injection compromises system behavior, unsafe output handling amplifies blast radius into backend systems, and monitoring blind spots prevent detection until business impact occurs. An AI application security program should close four vulnerability categories from the OWASP LLM Top 10: prompt injection (LLM01), insecure output handling (LLM02), supply chain vulnerabilities (LLM05), and excessive agency (LLM06). System prompt integrity reduces—but does not eliminate—the risk of user input overriding system instructions. Structured prompt formats help distinguish system instructions from user content, but attackers bypass delimiters through semantic manipulation, so delimiter-based separation should be combined with input screening, least-privilege, and output validation.