cs.CRMar 16, 2026

TrinityGuard: A Unified Framework for Safeguarding Multi-Agent Systems

Authors: Kai Wang, Biaojie Zeng, Zeming Wei, Chang Jin, Hefeng Zhou, Xiangtian Li, Chao Yang, Jingjing Qu, +2 more

Organizations: Shanghai AI Laboratory

Abstract

With the rapid development of LLM-based multi-agent systems (MAS), their significant safety and security concerns have emerged, which introduce novel risks going beyond single agents or LLMs. Despite attempts to address these issues, the existing literature lacks a cohesive safeguarding system specialized for MAS risks. In this work, we introduce TrinityGuard, a comprehensive safety evaluation and monitoring framework for LLM-based MAS, grounded in the OWASP standards. Specifically, TrinityGuard encompasses a three-tier fine-grained risk taxonomy that identifies 20 risk types, covering single-agent vulnerabilities, inter-agent communication threats, and system-level emergent hazards. Designed for scalability across various MAS structures and platforms, TrinityGuard is organized in a trinity manner, involving an MAS abstraction layer that can be adapted to any MAS structures, an evaluation layer containing risk-specific test modules, alongside runtime monitor agents coordinated by a unified LLM Judge Factory. During Evaluation, TrinityGuard executes curated attack probes to generate detailed vulnerability reports for each risk type, where monitor agents analyze structured execution traces and issue real-time alerts, enabling both pre-development evaluation and runtime monitoring. We further formalize these safety metrics and present detailed case studies across various representative MAS examples, showcasing the versatility and reliability of TrinityGuard. Overall, TrinityGuard acts as a comprehensive framework for evaluating and monitoring various risks in MAS, paving the way for further research into their safety and security.

Figures & tables

Explore similar work

CardsList
  1. SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems

    Sep 1, 2026Rui Yang, Junjie Xu, Zhengyu Liu +4AI Agent Security BenchmarksMulti-Agent System Security

  2. PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation

    May 8, 2026Bingyu Yan, Xiaoming Zhang, Jinyu Hou +4LLM Agent SecurityPrompt Injection Attacks on AI Agents

  3. MAS-Shield: A Defense Framework for Secure and Efficient LLM MAS

    Nov 28, 2025Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +8Multi-Agent LLM SystemsLLM Auditing