Large language model (LLM) agents are transforming AI capabilities, yet their tool access raises safety concerns. Researchers from Georgia Tech and Carnegie Mellon University propose a proactive framework for ensuring verifiable safety in LLM workflows. This approach utilizes System-Theoretic Process Analysis (STPA) to identify hazards and translate them into enforceable specifications for data flow and tool interactions. The innovative Model Context Protocol (MCP) framework enhances safety by structuring labels related to tool capabilities and trust levels, enabling precise control over agent actions. The research highlights the effectiveness of formal verification methods, demonstrated through experiments using Alloy, to block unsafe data flows without compromising functionality. By establishing formal guardrails, the framework aims to minimize risks like data leaks in enterprise environments, enhancing LLM agent reliability. This study signifies a shift from reactive to proactive safety measures, paving the way for scalable and trustworthy deployments of LLM agents in sensitive applications.
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