How to protect multi-agent apps from attacks?
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach
October 18, 2024
https://arxiv.org/pdf/2410.13454-
This paper proposes a new method for making multi-agent AI systems resilient to Byzantine attacks, where some agents can act maliciously and send incorrect information to disrupt the system.
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The key points for LLM-based multi-agents are:
- The method uses a hybrid approach combining error thresholds and triggering intervals to detect malicious agents, improving accuracy compared to using only one method.
- This approach reduces communication needs between agents by using a minimal event-triggered interval (MEI), meaning communication doesn't happen constantly.
- The paper demonstrates this method working for agents with different capabilities, not just identical ones, which is relevant to LLMs having varying roles.
- A key assumption is that the attackers aren't specifically designed to fool THIS detection method, highlighting a real-world limitation even with this improvement.