Policy Guided Secret Leakage Auditing in Plugin Based Financial LLM Workflows and Tool Calls

Abstract

The integration of Large Language Models into the financial sector has revolutionized how institutions handle customer service, portfolio management, and automated trading. To extend their capabilities, these models increasingly rely on plugin-based architectures that allow them to interact with external databases, execution environments, and third-party application programming interfaces. However, this extended functionality introduces severe security vulnerabilities, particularly concerning the leakage of highly sensitive financial secrets such as personally identifiable information, trading algorithms, and access credentials. This paper presents a comprehensive, policy-guided secret leakage auditing framework specifically designed for plugin-based Large Language Model workflows within financial services. By deploying a dynamic flow analysis engine coupled with a declarative policy specification language, the proposed system is capable of tracking sensitive data elements as they propagate through the model and its interconnected plugins. The auditing mechanism evaluates data transmissions against pre-defined compliance policies derived from financial regulations, flagging unauthorized data exfiltration before it occurs. Extensive evaluation in simulated banking environments demonstrates that the framework achieves high detection accuracy with minimal computational overhead, thereby ensuring that financial institutions can safely harness the power of artificial intelligence without compromising data security. The findings underscore the critical necessity of implementing robust, policy-driven safeguards in the rapidly evolving landscape of financial technology.