The paper explores how Large Language Models (LLMs) are reshaping various fields, from political science to software development. While these models provide significant value, their limitations arise from being trained on static data and needing fine-tuning for specific tasks. AI agents, which utilize LLMs, can overcome some of these issues by leveraging real-time data and external tools, making them essential in applications like live weather reporting and advanced data analysis.
In industrial contexts, the integration of AI agents facilitates near-autonomous operations, enhancing productivity and enabling real-time decision-making. However, the advancement of these technologies also exposes them to significant security vulnerabilities, notably prompt injection attacks. Such threats can compromise the reliability and integrity of AI agents, underscoring the need for robust security measures. This paper introduces a comprehensive framework incorporating Role-Based Access Control (RBAC), aiming to reinforce security and ensure scalable deployment of AI agents, particularly within on-premises environments.
👉 Pročitaj original: arXiv AI Papers
