2 posts
AI agents deployed in enterprise environments are highly susceptible to indirect prompt injection attacks, enabling data theft and unauthorized actions. Security teams must adopt an 'assume breach' architecture for LLMs, focusing on blast radius reduction through agent sandboxing, credential isolation, egress restrictions, and human-in-the-loop governance.
Unit 42 researchers demonstrated a red-teaming methodology against Amazon Bedrock's multi-agent applications, highlighting the risks of prompt injection in orchestrated AI systems. By systematically bypassing agent guardrails, attackers can extract sensitive instructions, map tool schemas, and invoke integrated tools with malicious inputs, though built-in Bedrock Guardrails effectively mitigate these threats.
