As organizations increasingly incorporate generative AI for competitive advantage, a critical vulnerability has emerged: prompt injection attacks, which manipulate AI systems through cleverly crafted text inputs without requiring any coding. These attacks, identified as the top security risk for Large Language Model (LLM) applications, exploit the lack of clear distinctions between system and user inputs, potentially leading to technical, legal, financial, and reputational issues. The article discusses various types of prompt injection attacks, including direct, code, recursive, and jailbreaking techniques, and describes how attackers use authoritative language and psychological manipulation to override legitimate system prompts. To counter these threats, the text outlines strategies for detection and prevention, such as comprehensive logging, anomaly detection, red team exercises, and specialized AI evaluation tools, advocating for a defense-in-depth approach that includes secure prompt engineering, rigorous input validation, and output verification protocols. These measures aim to create a robust security posture, protect AI systems from becoming liabilities, and maintain the benefits of AI deployments, with platforms like Galileo offering advanced tools to enhance AI security.