Secure Multi-Agent AI with AWS Cedar: Prevent Privilege Abuse

Multi-agent AI systems face a risk of authorization scope expanding as agents delegate tasks. This means an agent could act beyond what the original user intended, even with existing security measures. This risk is classified as ASI03: Identity & Privilege Abuse. A new approach uses a three-layer policy model with Cedar, an open-source authorization policy language, deployed on Amazon Web Services. This model helps prevent agents from gaining unauthorized access. The implementation uses OAuth 2.0 for authentication and Cedar for authorization. The system uses two AWS Lambda functions. One function normalizes requests and signs the user context to prevent tampering. The other evaluates three independent policy layers, stopping if a denial is found. These policy layers check various aspects. Layer one verifies the invoking agent's trust score, namespace, and lifecycle stage. Layer two ensures the delegation hop count is within a limit of five and that requested tasks are part of the target agent's capabilities. Layer three checks if the originating human user has the required role, has completed multi-factor authentication, and is within the allowed delegation depth. This matters because it helps secure multi-agent AI systems by preventing unauthorized actions.