mlsanityData to 14 Oct 2026

TrinityGuard: A Unified Framework for Safeguarding Multi-Agent Systems

arXiv 2603.15408 ↗First posted 16 Mar 2026cs.CR cs.AI cs.CLcited by 5

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 43 of its 56 references that are in the map. First posted on arXiv 16 Mar 2026.

Builds on

43 of 56 references.

Closest ancestors · 18

Same direction, newest first

  1. 2025Shadows in the Code: Exploring the Risks and Defenses of LLM-based Multi-Agent Software Development Systems23 Nov
  2. 2025Securing Multi-Agent Systems Against Corruptions via Node Contribution Backpropagation22 Oct
  3. 2025VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation3 Oct
  4. 2025AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models30 Sep
  5. 2025Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAS5 Aug
  6. 2025From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows29 Jun
  7. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures24 Jun
  8. 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents31 May
  9. 2025Security of Internet of Agents: Attacks and Countermeasures12 May
  10. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  11. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks20 Feb
  12. 2025Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety2 Feb
6 more
  1. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
  2. 2024Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems9 Oct
  3. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies28 Jul
  4. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  5. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast13 Feb
  6. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agents18 Jan

Other references · 25

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  4. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
  5. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  6. 2023Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrationscited by 431
  7. 2024Frontier Models are Capable of In-context Schemingcited by 287
  8. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 286
  9. 2024TrustLLM: Trustworthiness in Large Language Modelscited by 285
  10. 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
  11. 2023SafetyBench: Evaluating the Safety of Large Language Modelscited by 199
  12. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
13 more
  1. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 128
  2. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
  3. 2023Jatmo: Prompt Injection Defense by Task-Specific Finetuningcited by 106
  4. 2025HalluLens: LLM Hallucination Benchmarkcited by 92
  5. 2023Effective Prompt Extraction from Language Modelscited by 85
  6. 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 71
  7. 2025Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systemscited by 62
  8. 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
  9. 2025EvoAgentX: An Automated Framework for Evolving Agentic Workflowscited by 44
  10. 2023Misusing Tools in Large Language Models With Visual Adversarial Examplescited by 42
  11. 2025The Traitors: Deception and Trust in Multi-Agent Language Model Simulationscited by 34
  12. 2024Efficient Tool Use with Chain-of-Abstraction Reasoningcited by 33
  13. 2025Stay Focused: Problem Drift in Multi-Agent Debatecited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
  2. 2025Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS
  3. 2026Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions
  4. 2026ClawSentry: A Progressive Multi-Tier Security Monitor for Safeguarding Autonomous LLM Agents
  5. 2025Extending the OWASP Multi-Agentic System Threat Modeling Guide: Insights from Multi-Agent Security Research
  6. 2025Toward a Safe Internet of Agents
  7. 2026PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation
  8. 2026Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents