Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale
AI agents interacting in networks introduce new risks that do not appear when agents are tested in isolation. Actions such as malicious message propagation can cascade across multiple agents, extracting private data and involving otherwise uninvolved agents. While early signs show some networks may develop resistance to attacks, effective defenses remain an open challenge.
- ▪Some risks in AI agent networks, like propagation and amplification, only emerge through interactions between agents.
- ▪In tests, a single malicious message was able to spread across multiple agents, collecting private data and pulling in uninvolved agents.
- ▪Agent networks can rapidly share information, but this also allows failures and attacks to spread quickly, undermining reliability.
- ▪Early defensive behaviors were observed in a small fraction of agents, limiting the spread of some attacks.
- ▪Trust capture and invisibility are risks where attackers manipulate verification systems and hide the source of malicious activity within agent chains.
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Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale Published April 30, 2026 By Gagan Bansal , Principal Researcher Shujaat Mirza , Security Researcher II Keegan Hines , Principal AI Safety Researcher Will Epperson , Senior Research Software Engineer Zachary Huang , Senior Researcher Whitney Maxwell , Senior Security Researcher Pete Bryan , Principal AI Security Researcher Tyler Payne , Senior Research Software Engineer Adam Fourney , Senior Principal Researcher Amanda Swearngin , Principal Researcher Wenyue Hua , Senior Researcher Tori Westerhoff , Principal Director Maya Murad , Senior Technical PM, AI Frontiers Ece Kamar , CVP and Lab Director of AI Frontiers Ram Shankar Siva Kumar , Partner Research Lead Saleema Amershi , Partner Research…
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