mlsanityData to 14 Oct 2026

WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies

arXiv 2609.05591 ↗First posted 4 Sep 2026physics.soc-ph cs.AI cs.SIcited by 0

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Abstract (arXiv)

Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-agent fraction and society size in a controlled financial agent society, where agents communicate over a social network and trade in a shared market. In the primary financial scenario, collective failure requires broad harmful diffusion together with severe price dislocation or liquidity stress. Across all tested society sizes, failure remains rare at low harmful fractions but rises sharply over a narrow range. As society size grows from N=100 to N=2000, the harmful fraction associated with a 50% failure probability decreases from 4.7% to 2.2%, while the corresponding number of harmful agents increases from approximately 5 to 44. In contrast, when the number of harmful agents is held fixed, their impact becomes weaker as the society grows. Controlled interventions further show that broader network reach shifts the collapse boundary toward lower harmful fractions, whereas stronger conformity alone has little effect. To characterize these effects, we introduce Agent Society Dynamics, a finite-size framework for relating harmful-agent fraction, society size, and interaction structure to collective failure. Overall, our results reveal a nonlinear, size-dependent collapse transition in financial agent societies, showing that collective failure depends not only on the prevalence of harmful agents but also on the size and interaction structure of the surrounding society. Code is available at https://github.com/SAIL-Research-Lab/WolfSociety.

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References · 22

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  3. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agentscited by 306
  4. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 186
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  9. 2026Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphscited by 20
  10. 2026Colosseum: Auditing Collusion in Cooperative Multi-Agent Systemscited by 18
  11. 2025Group size effects and collective misalignment in LLM multi-agent systemscited by 16
  12. 2026Conformity and Social Impact on AI Agentscited by 16
10 more
  1. 2026MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBookcited by 12
  2. 2026Conformity Generates Collective Misalignment in AI Agents Societiescited by 9
  3. 2025MAEBE: Multi-Agent Emergent Behavior Frameworkcited by 8
  4. 2025When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platformscited by 8
  5. 2026Evaluating Collective Behaviour of Hundreds of LLM Agentscited by 8
  6. 2026Emergent Risks in Generative Multi-Agent Systemscited by 5
  7. 2026Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lenscited by 5
  8. 2026Human Values Matter: Investigating How Misalignment Shapes Collective Behaviors in LLM Agent Communitiescited by 4
  9. 2026Emergence World: A Platform for Evaluating Long-Horizon Multi-Agent Autonomycited by 4
  10. 2026Social Networks of LLM Agentscited by 2

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