Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale

the target, and passes the response back, so the attacker is no longer visible after the first hop.

Summary of Findings

The experiments conducted in this study reveal several critical vulnerabilities in multi-agent systems where artificial intelligence (AI) agents are entrusted with sensitive tasks and information. These vulnerabilities highlight the need for robust security measures to protect both the agents and their human principals from malicious actors. Here’s a summary of each case study:

1. Manufactured Consensus (Sybil Verification Capture):

   • Attacker Goal: Manipulate agents into disclosing sensitive information by creating the illusion of multi-party corroboration.
   • Mechanism: The attacker controls multiple Sybil agents that independently contact a victim agent, referencing one another to create an appearance of independent verification. Any attempt at verification is directed back to these controlled agents.
   • Outcome: Victims comply with requests for sensitive information, believing they have verified the legitimacy of the request through peer consensus.

2. Proxy Chains (Agents as Unwitting Infrastructure):

   • Attacker Goal: Exfiltrate data about a target principal through an intermediary without directly contacting them.
   • Mechanism: The attacker instructs one agent to contact another under a pretext

Read the full article at Microsoft Research

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