Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to semantic attacks that can trigger cascading failures across the network. Current defenses face a fundamental dilemma: lightweight single-auditor methods are prone to single points of failure, while robust committee-based approaches (e.g., voting) incur prohibitive computational costs in multi-turn interactions. To resolve this tension, we propose MAS-Shield, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline. Rather than applying uniform scrutiny, MAS-Shield dynamically allocates resources by first identifying high-influence nodes via Critical Agent Auditing Selection. These nodes undergo Light Auditing using lightweight sentry models to rapidly filter benign cases, escalating only high-risk signals to a heavyweight Two-Round Consensus mechanism. This hierarchical design optimizes the security-efficiency trade-off. Experiments demonstrate that MAS-Shield achieves a 92.5% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70% compared to existing methods.