Vulnerability Analysis for Safe Multi-Agent Reinforcement

Recent security analyses highlight critical vulnerabilities across different domains. A new research paper on arXiv:2610.27293v1 examines inference-time observation attacks against Safe Multi-Agent Reinforcement Learning (SMARL), introducing the MinMax Attack framework to target compromised agents. Simultaneously, a vulnerability surface analysis of the Gate protocol, which manages $7.34 B in TVL across Ethereum and L2s, evaluates its multi-chain liquidity aggregation architecture, specifically focusing on the GateRouter and various composable vaults used for yield optimisation.