The rise of autonomous AI agents within business software demands a fresh approach to security. Unlike earlier chatbot tools, modern agents act with real privileges, such as updating databases, calling microservices, composing and even executing code, or triggering workflows on their own. This shift expands the blast radius of any flaw or compromise. As one Microsoft analysis observes, today’s AI agents “can update database records, trigger enterprise workflows, access sensitive data, and interact with production systems all autonomously.” In practice, that means a mistake or exploit can have immediate operational impact instead of just a reputational cost.With agents in the loop, input manipulation becomes especially dangerous. Prompt-injection attacks let adversaries commandeer an AI by feeding it malicious instructions in user inputs or hidden in external data. A carefully crafted prompt or document can cause an agent to reveal secrets or perform harmful actions. These manipulations can be direct (an attacker’s text overriding the agent’s instructions) or indirect (for example, hidden commands embedded in HTML or metadata that the agent ingests). By definition, even inputs imperceptible to humans can subvert the model, forcing it to break safety rules. In effect, prompt injection can trick an AI into disclosing internal prompts, executing arbitrary commands, or making unauthorized changes.