Enhancing Security in GenAI Multi-Agent Systems

The field of GenAI multi-agent systems is rapidly evolving, with a growing focus on addressing the unique security challenges posed by these systems. Recent research has highlighted the need for standardized protocols to enable secure interaction between agents and external tools, as well as the importance of developing comprehensive threat models to mitigate potential risks. Noteworthy papers in this area include Securing GenAI Multi-Agent Systems Against Tool Squatting, which proposes a zero-trust registry-based approach to prevent tool squatting attacks, and Securing Agentic AI, which introduces a comprehensive threat model and mitigation framework for GenAI agents. Additionally, papers such as AegisLLM and ACE have demonstrated the effectiveness of cooperative multi-agent defense systems and secure architectures for LLM-integrated app systems in enhancing the security and robustness of GenAI systems.

Sources

Securing GenAI Multi-Agent Systems Against Tool Squatting: A Zero Trust Registry-Based Approach

Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents

An Algebraic Approach to Asymmetric Delegation and Polymorphic Label Inference (Technical Report)

did:self A registry-less DID method

AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security

ACE: A Security Architecture for LLM-Integrated App Systems

PICO: Secure Transformers via Robust Prompt Isolation and Cybersecurity Oversight

SAGA: A Security Architecture for Governing AI Agentic Systems

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