AI SAFETY BENCHMARK
MCP-SafetyBench: A Benchmark for Safety Evaluation of Large Language Models with Real-World MCP Servers
Introducing MCP-SafetyBench, a comprehensive benchmark for evaluating LLM agents in real-world Model Context Protocol (MCP) environments. It reveals significant safety vulnerabilities and the urgent need for stronger defenses.
Key Findings at a Glance
Our evaluation across 13 leading LLMs exposes widespread vulnerabilities in MCP systems, a critical safety-utility trade-off, and significant disparities in defense capabilities.
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Avg. Attack Success Rate
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Avg. Task Success Rate (Clean)
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Attack Types Covered
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Real-World Domains
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overall Vulnerability
Attack Taxonomy
Mitigation Challenges
Widespread LLM Vulnerabilities
MCP-SafetyBench reveals that all tested LLMs are susceptible to attacks, with an average Attack Success Rate (ASR) of 39.88%. No model achieves both strong task performance and robust defense, indicating a clear safety-utility trade-off (r = -0.572). Vulnerabilities escalate with task complexity and server interactions.
Comprehensive Attack Taxonomy
Our benchmark covers 20 distinct attack types across three perspectives: MCP Server-side, MCP Host-side, and User-side. Server-side attacks are the most prevalent (74.69%), while Host-side attacks are most effective (81.94% ASR), highlighting critical vulnerabilities in agent coordination logic.
Limitations of Prompt-Based Defenses
Safety prompts show limited effectiveness, reducing ASR by only -1.22% overall. While beneficial for explicit malicious code execution, they can be ineffective or even counterproductive for semantic misalignment attacks, underscoring the need for multi-layered defense strategies beyond simple prompts.
Enterprise Process Flow (MCP Workflow)
User Instruction
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Agent Reasoning & Planning
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Tool Invocation
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MCP Server Execution
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Result Synthesis
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Final Response
81.94%
Average Attack Success Rate for Host-Side Attacks
| Benchmark | Real-World Integration | Multi-Step Tasks | MCP Server Attack | MCP Host Attack | MCP User Attack | Attack Types | Domains |
|---|---|---|---|---|---|---|---|
| SafeMCP | X | ✓ | X | X | X | 2 | 1 |
| MCPTox | ✓ | X | ✓ | X | X | 10 | 1 |
| MCIP-bench | X | X | X | ✓ | ✓ | 11 | 5 |
| MCP-AttackBench | X | X | X | X | X | 10 | 1 |
| MCPSecBench | X | X | ✓ | ✓ | ✓ | 17 | 5 |
| MCP-SafetyBench (Ours) | ✓ | ✓ | ✓ | ✓ | ✓ | 20 | 5 |
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could achieve with secure AI agent deployment.
Annual Savings
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Hours Reclaimed Annually
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Your Secure AI Implementation Roadmap
A structured approach to integrating AI agents safely and effectively into your enterprise operations.
Phase 1: Discovery & Threat Modeling
Assess current workflows, identify AI opportunities, and conduct a thorough threat model specific to your MCP environment.
Phase 2: Secure Agent & Tool Development
Design and develop agents with inherent security-by-design principles, implementing robust tool validation and access controls.
Phase 3: Integration & Testing (MCP-SafetyBench)
Integrate agents with real MCP servers and rigorously test against a comprehensive suite of adversarial scenarios using MCP-SafetyBench.
Phase 4: Monitoring & Adaptive Defenses
Deploy agents with continuous monitoring, anomaly detection, and mechanisms for adaptive defense against evolving threats.
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