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MCPHunt: An Evaluation Framework for Cross-Boundary Data Propagation in Multi-Server MCP Agents
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Introduces MCPHunt, a canary-based benchmark measuring non-adversarial cross-boundary credential propagation in multi-server MCP agents across 5 models, 147 tasks, and 9 mechanism families.
Problem
Multi-server MCP agents can propagate credentials across trust boundaries during faithful, non-adversarial task execution, yet no benchmark measures this compositional data-propagation risk.
Approach
Canary-based taint tracking with format-authentic secrets reduces propagation detection to string matching; environment-controlled risky/benign/hard-negative conditions and CRS stratification separate task-mandated transfer from policy-violating propagation.
Key results
- Policy-violating propagation rates range 11.5–41.3% across 5 models on risky environments.
- Prompt mitigation M3 reduces GPT-5.4 policy-violating propagation from 13.9% to 0.3% (97% relative reduction) with 80.5% utility.
- browser_to_local mechanism shows 74.4% propagation for GPT-5.4 versus 0% for indirect_exposure; mechanism family explains 62% of pseudo-R² improvement versus 32% for model identity.
Surprising findings
- Generic privacy reminders (M1) yield at most modest reductions and 0% change for DeepSeek-V4-Flash.
- Stronger utility does not imply lower propagation: MiniMax-M2.7 has the highest utility (92.2%) and highest propagation (45.2%).
Limitations
- Canaries are verbatim; paraphrased propagation is missed.
- All 147 tasks are researcher-designed synthetic tasks.
- Runtime taint guard is evaluated only post-hoc on existing traces.
Experimental setup
Datasets: 147 researcher-designed MCP tasks
Benchmarks: MCPHunt
Metrics: policy-violating propagation rate propagation rate utility rate Wilson 95% CI Cohen's kappa
Baselines: MCP-SafetyBench MCPSecBench MSB TOP-Bench AgentLeak
Builds on: Model Context Protocol TOP-Bench NeMo Guardrails
Closest papers by meaning
- MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers (NeurIPS 2026 · similarity 0.88)
- MCPTox: A Benchmark for Tool Poisoning on Real-World MCP Servers (AAAI 2026 · similarity 0.86)
- SecureClaw: Clawing Back Control of LLM Agents (NeurIPS 2026 · similarity 0.85)
- AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators (NeurIPS 2026 · similarity 0.85)
- MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents (NeurIPS 2026 · similarity 0.85)
- AI Agents May Always Fall for Prompt Injections (NeurIPS 2026 · similarity 0.85)
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