Security Vulnerabilities in Distributed AI Systems
Identifying the Achilles Heel of Multi-Agent LLM Networks
This research examines critical security vulnerabilities in Distributed Multi-Agent Systems (DMAS) that integrate multiple LLMs across various servers.
- Proposes a novel attack taxonomy for DMAS, including Malicious Agents, Free Riding, and Protocol Exploitation
- Demonstrates how these attacks can compromise system integrity through theoretical analysis and experimental validation
- Reveals significant vulnerabilities even in systems with robust security measures
- Serves as an essential red-teaming tool for evaluating and strengthening DMAS security
Why it matters: As organizations deploy increasingly complex multi-agent AI systems, understanding these security vulnerabilities becomes crucial for building trustworthy AI infrastructure and protecting against potential attacks.