Why AI agents are vulnerable to prompt injection and how to protect them at the architectural level

This Habr article explores the critical security challenge facing modern AI agents: vulnerability to prompt injection attacks. The author explains that even a standard document processed by a model can contain malicious instructions, potentially leading to data leaks or unauthorized actions if the agent has broad permissions. The material proposes a comprehensive approach to security at the system architecture level. Key focus areas include principles of trust separation, strict control over agent actions, and minimizing tool access rights. The author provides practical examples of implementing security mechanisms in Python, emphasizing the importance of designing 'secure by default' systems. This article is valuable for developers working with LLMs and deploying autonomous agents into business processes, as it offers concrete strategies to mitigate the risks of exploiting vulnerabilities within model call chains.