Large Language Model (LLM)-based Multi-Agent Systems (MAS) offer powerful cooperative reasoning but remain vulnerable to adversarial attacks, where compromised agents can undermine the system's overall performance. Existing defenses either depend on single trusted auditors, creating single points of failure, or sacrifice efficiency for robustness. To resolve this tension, we propose \textbf{AgentShield}, a distributed framework for efficient, decentralized auditing. AgentShield introduces a novel three-layer defense: \textbf{(i) Critical Node Auditing} prioritizes high-influence agents via topological analysis; \textbf{(ii) Light Token Auditing} implements a cascade protocol using lightweight sentry models for rapid discriminative verification; and \textbf{(iii) Two-Round Consensus Auditing} triggers heavyweight arbiters only upon uncertainty to ensure global agreement. This principled design optimizes the robustness-efficiency trade-off. Experiments demonstrate that AgentShield achieves a 92.5\% recovery rate and reduces auditing overhead by over 70\% compared to existing methods, maintaining high collaborative accuracy across diverse MAS topologies and adversarial scenarios.
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