Large Language Models (LLMs) are increasingly integrated into real-world applications via the Model Context Protocol (MCP), a universal open standard for connecting AI agents with data sources and external tools. While MCP enhances the capabilities of LLM-based agents, it also introduces new security risks and significantly expands their attack surface. In this paper, we present the first formalization of a secure MCP and its required specifications. Based on this foundation, we establish a comprehensive MCP security taxonomy that extends existing models by incorporating protocol-level and host-side threats, identifying 17 distinct attack types across four primary attack surfaces. Building on these specifications, we introduce MCPSecBench, a systematic security benchmark and playground that integrates prompt datasets, MCP servers, MCP clients, attack scripts, a GUI test harness, and protection mechanisms to evaluate these threats across three major MCP platforms. MCPSecBench is designed to be modular and extensible, allowing researchers to incorporate custom implementations of clients, servers, and transport protocols for rigorous assessment. Our evaluation across three major MCP platforms reveals that all attack surfaces yield successful compromises. Core vulnerabilities universally affect Claude, OpenAI, and Cursor, while server-side and specific client-side attacks exhibit considerable variability across different hosts and models. Furthermore, current protection mechanisms proved largely ineffective, achieving an average success rate of less than 30%. Overall, MCPSecBench standardizes the evaluation of MCP security and enables rigorous testing across all protocol layers.