mlsanityData to 7 Oct 2026

An Adversary-Resistant Multi-Agent LLM System via Credibility Scoring

arXiv 2505.24239 ↗First posted 30 May 2025cs.MA cs.AI cs.CLcited by 10

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

Abstract (arXiv)

While multi-agent LLM systems show strong capabilities in various domains, they are highly vulnerable to adversarial and low-performing agents. To resolve this issue, in this paper, we introduce a general and adversary-resistant multi-agent LLM framework based on credibility scoring. We model the collaborative query-answering process as an iterative game, where the agents communicate and contribute to a final system output. Our system associates a credibility score that is used when aggregating the team outputs. The credibility scores are learned gradually based on the past contributions of each agent in query answering. Our experiments across multiple tasks and settings demonstrate our system's effectiveness in mitigating adversarial influence and enhancing the resilience of multi-agent cooperation, even in the adversary-majority settings.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 18 of its 44 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

18 of 44 references.

References · 18

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  5. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  6. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  7. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  8. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  9. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  10. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  11. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  12. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 420
6 more
  1. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
  2. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 221
  3. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 201
  4. 2024MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debatecited by 54
  5. 2024Smoothie: Label Free Language Model Routingcited by 31
  6. 2024Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agentscited by 22

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 54
  2. 2025SoK: Trust-Authorization Mismatch in LLM Agent Interactionscited by 11
  3. 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
  4. 2026When Latent Agents Lie: KV-Cache Integrity in Multi-Agent LLM Collaborationcited by 2
  5. 2026Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systemscited by 1
  6. 2026MetaResearcher: Scaling Deep Research via Self-Reflective Reinforcement Learning in Adversarial Virtual Environmentscited by 1
  7. 2026Agents that Matter: Optimizing Multi-Agent LLMs via Removal-Based Attributioncited by 0
  8. 2026Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systemscited by 0
  9. 2026Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Modelscited by 0
  10. 2026MiniRep: Robust Reputation-Based Aggregation for Multi-Agent Debatecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Defending LLM-based Multi-Agent Systems Against Cooperative Attacks with Sentence-Level Rectification
  2. 2025TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems
  3. 2025Red-Teaming LLM Multi-Agent Systems via Communication Attacks
  4. 2026SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems
  5. 2026Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate
  6. 2025To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
  7. 2025SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
  8. 2026SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models