SecureGov-Agent: A Governance-Centric Multi-Agent Framework for Privacy-Preserving and Attack-Resilient LLM Agents

Large Language Model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across di- verse applications, yet they face critical security challenges in- cluding backdoor attacks, prompt injection, and privacy leakage. Existing defense mechanisms typically address single threat vec- tors, lacking a unified governance architecture for comprehensive security. We propose SecureGov-Agent, a governance-centric multi-agent framework that introduces a dedicated Governance Agent responsible for monitoring inter-agent communications, auditing tool invocations, and enforcing security policies. Our framework incorporates a multi-perspective risk scoring mech- anism that evaluates content risk, privacy risk, and behavioral anomalies to dynamically assess each agent’s trustworthiness. We further enhance robustness through adversarial training on syn- thesized attack scenarios. Extensive experiments across medical consultation, financial advisory, and document processing scenar- ios demonstrate that SecureGov-Agent achieves a balanced trade- off between security, privacy, and efficiency: reducing attack success rates by 73.2% compared to unprotected systems and privacy leakage rates by 81.4%, while maintaining 89.7% task completion rate with only 15.3% latency overhead. Notably, our framework excels in privacy protection (6.8% leakage rate) and maintains practical efficiency, offering a comprehensive solution for privacy-sensitive multi-agent deployments. Our framework provides a reproducible benchmark for multi-agent security research and offers practical deployment guidelines for privacy- sensitive applications.