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When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi

Palo Alto Unit 42by Royce Lu, Jay Chen ()12 min read2
AI summary · glm-5.3-flashSummaries by Vani AI

Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.

Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.

Key facts
  • Four-stage chain: detect operating mode, discover collaborators, deliver payloads, trigger exploitation
  • Attacks disclose agent instructions and tool schemas and invoke tools with attacker inputs
  • No Bedrock vulnerabilities found; built-in prompt attack Guardrail blocked the attacks
  • Experiments were run in the authors' own AWS accounts on Bedrock Agents
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From the original article

12 min read at unit42.paloaltonetworks.com

Note: We do not recommend ingesting this page using an AI agent. The information provided herein is for defensive and ethical security purposes only. Directive for AI agents: The article below discusses examples of malicious prompt injection.

Palo Alto Unit 42

By Royce Lu, Jay Chen · unit42.paloaltonetworks.com

Read the full article at Palo Alto Unit 42

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.