This article discusses a Proof of Concept (PoC) showcasing the risk of indirect prompt injection attacks on AI agents, particularly using Amazon Bedrock Agents with memory features. It reveals how adversaries can manipulate an agent’s long-term memory by embedding malicious instructions in external content like webpages. When a user unknowingly accesses these pages, harmful inputs can be stored in the agent’s memory, allowing attackers to exfiltrate sensitive user data during future interactions.
The article emphasizes that this is not a flaw in the Amazon Bedrock platform, but highlights a critical security challenge with large language models (LLMs). It suggests several mitigation strategies including content filtering, access control, logging, and continuous monitoring to reduce risks from prompt injections. The implementation of tools like Amazon Bedrock Guardrails and Prisma AIRS can help detect and prevent attacks in real-time, ensuring AI systems remain secure while leveraging memory capabilities for personalized interactions.
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