mlsanityData to 14 Oct 2026

Is Monitoring Enough? Strategic Agent Selection For Stealthy Attack in Multi-Agent Discussions

arXiv 2603.21194 ↗First posted 22 Mar 2026cs.CR cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Multi-agent discussions have been widely adopted, motivating growing efforts to develop attacks that expose their vulnerabilities. In this work, we study a practical yet largely unexplored attack scenario, the discussion-monitored scenario, where anomaly detectors continuously monitor inter-agent communications and block detected adversarial messages. Although existing attacks are effective without discussion monitoring, we show that they exhibit detectable patterns and largely fail under such monitoring constraints. But does this imply that monitoring alone is sufficient to secure multi-agent discussions? To answer this question, we develop a novel attack method explicitly tailored to the discussion-monitored scenario. Extensive experiments demonstrate that effective attacks remain possible even under continuous monitoring, indicating that monitoring alone does not eliminate adversarial risks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 22 Mar 2026.

Builds on

29 of 42 references.

References · 29

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  5. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  6. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  7. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  8. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  9. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  10. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  11. 2024Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Modelscited by 268
  12. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attackscited by 122
17 more
  1. 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communitiescited by 86
  2. 2024Self-Evolving Multi-Agent Collaboration Networks for Software Developmentcited by 74
  3. 2025G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systemscited by 59
  4. 2024MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debatecited by 56
  5. 2025MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systemscited by 50
  6. 2024Agent-Oriented Planning in Multi-Agent Systemscited by 36
  7. 2025Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Modelscited by 27
  8. 2025MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulationscited by 27
  9. 2025Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAScited by 17
  10. 2025MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systemscited by 10
  11. 2025Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Predictioncited by 9
  12. 2025SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMscited by 8
  13. 2013Opinion Maximization in Social Networkscited by 6
  14. 2025Can an Individual Manipulate the Collective Decisions of Multi-Agents?cited by 5
  15. 2026Automatic Method Illustration Generation for AI Scientific Papers via Drawing Middleware Creation, Evolution, and Orchestrationcited by 5
  16. 2026DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generationcited by 4
  17. 2023Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approachcited by 1

Built on by

Not cited yet.