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Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning

cs.AI updates on arXiv.org·
AI Analysis

A new framework called HAM$^{3}$ has been developed to explore vulnerabilities in multi-modal multi-agent systems (MM-MAS) through hierarchical attacks. The framework reveals significant weaknesses, achieving an Attack Success Rate of up to 78.3%, highlighting the need for improved robustness in these systems.

Key Topics

multi-modal multi-agent systemsHAM$^{3}$GQA benchmarkReAct

Originally reported by cs.AI updates on arXiv.org. Read the full article ↗

Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning | AI Crypto Daily Wire