Tool-Flow Taint Analysis for Data Exfiltration Defense in Large Language Model Agents

Abstract

The rapid integration of Large Language Models into autonomous agentic systems has introduced unprecedented capabilities for task automation, reasoning, and tool utilization. However, equipping these agents with the ability to access external APIs, internal databases, and arbitrary web resources significantly broadens the attack surface for data exfiltration. Malicious actors can leverage prompt injection and indirect prompt injection techniques to hijack the agent control flow, forcing it to retrieve sensitive information and transmit it to attacker-controlled destinations. Existing security mechanisms, which predominantly rely on static prompt filtering or heuristic anomaly detection, fail to maintain context across complex, multi-step tool invocations. This paper introduces a comprehensive framework based on Tool-Flow Taint Analysis designed to mitigate data exfiltration in Large Language Model agents. By adapting classical dynamic taint analysis principles to the semantic and operational workflows of agent toolchains, the proposed architecture tracks sensitive data from predefined sources through intermediate natural language reasoning steps to potential external sinks. We establish robust semantic taint propagation rules capable of operating within the non-deterministic environment of Large Language Models. Extensive experimental evaluations demonstrate that the proposed system achieves high efficacy in intercepting exfiltration attempts while maintaining a low false-positive rate and negligible latency overhead. The findings provide a critical foundation for securing next-generation autonomous agents against sophisticated data-stealing attacks in enterprise environments.