Large Language Models fundamentally change how software behaves. Unlike traditional applications, LLM-based systems do not execute predefined logic alone — they interpret input, generate output dynamically, and often trigger downstream actions in connected tools and systems. This creates a new class of security risks that cannot be reliably assessed through static code reviews or classic penetration testing alone. Seemingly harmless user input can alter system behavior at runtime, bypass intended safeguards, or cause unintended actions. Attack vectors such as prompt injection, tool and agent abuse, and uncontrolled data exposure exploit exactly this dynamic nature. They do not rely on broken code, but on the model’s willingness to follow instructions — even when those instructions are indirect, hidden, or context-based. As LLMs are increasingly integrated into business-critical workflows, APIs, and automation chains, their security directly impacts data protection, system integrity, and operational resilience. Understanding these risks therefore requires hands-on security testing that reflects real interactions, real architectures, and real attacker behavior. LLM security is not a theoretical problem. It is an operational risk that must be addressed proactively — through practical testing, controlled experimentation, and security-by-design approaches tailored to AI systems.