Securing Multi-Agent AI Systems
A Hierarchical Framework for LLM-based Agent Safety
AgentSafe introduces a security framework for LLM-based multi-agent systems through hierarchical information management and memory protection.
- Classifies data by security levels, restricting sensitive information access
- Implements memory protection mechanisms to prevent unauthorized data breaches
- Establishes data access controls between collaborating AI agents
- Addresses critical security vulnerabilities in increasingly autonomous AI systems
This research provides essential security infrastructure as organizations deploy collaborative AI agents in sensitive environments, helping prevent data leakage and unauthorized access in multi-agent ecosystems.