mlsanityData to 14 Oct 2026

MCPHunt: An Evaluation Framework for Cross-Boundary Data Propagation in Multi-Server MCP Agents

arXiv 2604.27819 ↗First posted 30 Apr 2026cs.AIcited by 1

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Abstract (arXiv)

Multi-server MCP agents create an information-flow control problem: faithful tool composition can turn individually benign read/write permissions into cross-boundary credential propagation -- a structural side effect of workflow topology, not necessarily malicious model behavior. We present MCPHunt, to our knowledge the first controlled benchmark that isolates non-adversarial, verbatim credential propagation across multi-server MCP trust boundaries, with three methodological contributions: (1) canary-based taint tracking that reduces propagation detection to objective string matching; (2) an environment-controlled coverage design with risky, benign, and hard-negative conditions that validates pipeline soundness and controls for credential-format confounds; (3) CRS stratification that disentangles task-mandated propagation (faithful execution of verbatim-transfer instructions) from policy-violating propagation (credentials included despite the option to redact). Across 3,615 main-benchmark traces from 5 models spanning 147 tasks and 9 mechanism families, policy-violating propagation rates reach 11.5--41.3% across all models. This propagation is pathway-specific (25x cross-mechanism range) and concentrated in browser-mediated data flows; hard-negative controls provide evidence that production-format credentials are not necessary -- prompt-directed cross-boundary data flow is sufficient. A prompt-mitigation study across 3 models reduces policy-violating propagation by up to 97% while preserving 80.5% utility, but effectiveness varies with instruction-following capability -- suggesting that prompt-level defenses alone may not suffice. Code, traces, and labeling pipeline are released under MIT and CC BY 4.0.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

13 of 30 references.

References · 13

  1. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agentscited by 369
  2. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  3. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agentscited by 306
  4. 2024BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practicescited by 114
  5. 2025MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocolscited by 51
  6. 2025MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agentscited by 42
  7. 2025MCP-SafetyBench: A Benchmark for Safety Evaluation of Large Language Models with Real-World MCP Serverscited by 34
  8. 2025The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaborationcited by 20
  9. 2025Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigationcited by 11
  10. 2024Benchmark Data Repositories for Better Benchmarkingcited by 10
  11. 2026Optimizing Agent Planning for Security and Autonomycited by 10
  12. 2026Information-Theoretic Privacy Control for Sequential Multi-Agent LLM Systemscited by 8
1 more
  1. 2025Trivial Trojans: How Minimal MCP Servers Enable Cross-Tool Exfiltration of Sensitive Datacited by 6

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Citing papers per year: 2026: 1.

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  1. 2026MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
  2. 2026TrustShiftProbe: Characterizing, Benchmarking, and Defending Staged Trust Attacks on MCP Servers
  3. 2026Trust propagation and structural containment in Multi-agent LLM pipelines
  4. 2026From Tool Connection to Execution Control: Benchmarking Security Invariants in MCP-Style Agent Runtimes
  5. 2025Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills
  6. 2025MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System
  7. 2026Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures
  8. 2026AgentSecBench: Measuring Prompt Injection, Privacy Leakage, and Tool-Use Integrity in LLM Agents