The article discusses strategies to build secure multi-agent systems in artificial intelligence applications by implementing guardrails and security measures against various types of attacks such as jailbreaks, session poisoning, and sensitive data exposure. Two approaches are detailed: using a second language model for judgment (Model Armor) and employing Google Cloud's Model Armor service for enterprise-scale solutions. Key components include responsible AI practices, prompt injection detection, protection against sensitive data leakage, malicious URL identification, and document screening capabilities. The article emphasizes the importance of preventing unsafe content from being saved in session history to mitigate long-term risks.
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