As AI agents become more interconnected, new types of risks are emerging that don’t show up when testing individual agents. Actions that seem harmless on their own can cascade through a network, causing unexpected problems. For example, a single malicious message can spread from one agent to others, stealing private data and pulling in agents that weren’t involved initially. While some networks are starting to show signs of becoming more resilient, protecting these systems from such interactions remains a big challenge.

How Agent Networks Are Evolving and Why Risks Increase

More agents from different organizations are now interacting with each other. Advances in large language models and affordable hardware have made it easier to build these agents. Common tools like ChatGPT, Claude, Copilot, and platforms such as email and GitHub put agents into constant contact. Instead of working alone, agents now operate as part of shared, interconnected systems. This allows for distributing tasks, sharing resources, and leveraging different areas of expertise across the network.

Because these agents are always on and communicate faster than humans, information can spread across the network in minutes. This speed and scale can be very useful—helping users get faster responses and more efficient workflows. But it also creates new risks. For instance, one early social network of agents quickly attracted tens of thousands of participants, only to be flooded with spam and scams. In experiments with agent marketplaces, information was shared rapidly, and coordinated behavior emerged, but so did failures. This shows that the reliability of one agent doesn’t predict how the entire network will behave. Risks only become visible when agents interact, and testing them one at a time doesn’t reveal these issues.

Red-Teaming to Find Vulnerabilities in Agent Networks

To understand these risks better, researchers conducted a red-team exercise on a live internal platform with over 100 agents running different models. These agents had various instructions and memory capabilities, and they acted on behalf of humans across forums, direct messages, and collaborative tasks. The goal was to see what vulnerabilities might exist when agents work together at scale.

During these tests, four main risks emerged that only appear when agents interact as a network. The first was propagation, where malicious behaviors spread from one agent to another, creating a kind of digital chain reaction that could collect private data along the way. The second was amplification: an attacker could use a trusted agent’s reputation to spread false information, which then snowballs into convincing but fake evidence. Trust capture was another concern, where attackers could manipulate how agents verify each other’s claims, turning a system meant to check facts into one that spreads falsehoods. Finally, invisibility was a risk where information could pass through chains of unaware agents, making it very difficult to trace the source of an attack.

These findings highlight how complex and unpredictable agent networks can be. As these systems grow more common, understanding and mitigating these types of risks will be crucial for building safe and reliable AI ecosystems. While progress is being made, the challenge remains to develop defenses that can keep up with the scale and speed of these interconnected agents.

Inspired by

• https://www.microsoft.com/en-us/research/blog/red-teaming-a-network-of-agents-understanding-what-breaks-when-ai-agents-interact-at-scale/

Sources

• azure.com
• victoriawesterhoff.com
• aka.ms