Research map / Multi-agent systems and games / LLM agents
MCPTox: A Benchmark for Tool Poisoning on Real-World MCP Servers
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Introduces MCPTox, a benchmark of 1348 tool-poisoning test cases built on 45 real-world MCP servers, and evaluates 20 LLM agent settings, finding high attack success rates and very low refusal.
Problem
Tool Poisoning Attacks, where malicious instructions are hidden in MCP tool metadata, have only been shown in isolated cases; their large-scale practical impact on LLM agents against real MCP servers is unquantified.
Approach
A benchmark that injects LLM-generated poisoned tool descriptions (three attack paradigms, trigger-condition/action/justification design) into real MCP server toolsets, where the malicious action is executed by a legitimate tool rather than the poisoned one, and measures ASR and refusal across many agents.
Key results
- o1-mini reaches the highest average attack success rate of 72.8% across settings, with Phi-4 at 70.2% and DeepSeek-R1 at 70.9%.
- Adapting InjecAgent payloads to tool descriptions drops Qwen3-8b ASR to 0.1% (reasoning on) and 0.0% (off), versus 41.8% and 14% for MCPTox.
- Claude-3.7-Sonnet has the highest refusal rate at under 3% of failures refused.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Enable reasoning mode in Qwen3-8b (ablation) | Qwen3-8b · MCPTox | average ASR: average ASR increased by 27.8% (text); 14.0 to 41.8 on the average column | Helped |
| Scale Qwen3 from 8B to 32B (reasoning enabled) (ablation) | Qwen3 family · MCPTox | average ASR: 41.8 to 58.5 (reasoning mode) | Helped |
| Use Implicit Trigger - Parameter Tampering paradigm (P3) instead of other paradigms (ablation) | attack paradigm · MCPTox | average ASR across agents: P3 46.7% vs P1 36.7% vs P2 26.7% | Helped |
| Add enhanced hijacking prompt (Enhanced Setting 1, IMPORTANT tags) (ablation) | all agents · MCPTox | average ASR: +2.0% average ASR | Helped |
| Add enhanced hijacking prompt (Enhanced Setting 2, Ignore all previous instructions) (ablation) | all agents · MCPTox | average ASR: +2.6% average ASR | Helped |
| Adapt IPI (InjecAgent) payloads into poisoned tool descriptions instead of purpose-built TPA payloads (ablation) | Qwen3-8b · InjecAgent payloads vs MCPTox | ASR: 41.8% MCPTox vs 0.1% InjecAgent (reasoning on) | Hurt |
| Scale up model size / reasoning (more capable models) | agents overall · MCPTox | ASR | Helped |
Surprising findings
- More capable models, larger sizes and enabled reasoning modes, tend to be more vulnerable (inverse scaling).
- Agents rarely refuse; refusal accounts for only a very small fraction of failures, and 18.9% of failures were direct execution of the poisoned tool.
Limitations
- Attack payloads are semi-automatically generated from human-defined paradigms rather than optimized against defenses.
- Evaluation covers only the paper's chosen agent pipeline and system prompt, with fixed hijacking prompt variants.
- Manual verification was needed to check LLM-generated attacks for hallucinated parameters.
Experimental setup
Datasets: MCP servers (45 real-world, from public MCP registries) InjecAgent payloads (adapted for comparison)
Benchmarks: MCPTox InjecAgent
Metrics: attack success rate refused ratio
Baselines: InjecAgent
Builds on: Model Context Protocol InjecAgent AgentDojo few-shot prompting
Closest papers by meaning
- Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks (NeurIPS 2026 · similarity 0.88)
- Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools (NeurIPS 2025 · similarity 0.88)
- Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning (ICML 2026 · similarity 0.88)
- Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents (ICML 2026 · similarity 0.88)
- AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents (NeurIPS 2024 · similarity 0.87)
- CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities (ICML 2025 · similarity 0.87)
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