arXiv:2604.27819v2 Announce Type: replace
Abstract: Multi-server MCP agents create an information-flow control (IFC) 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, exact-match 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 adds a matched alternative-value control (same tasks, distinct synthetic credentials); (3) completion-requires-secret (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 of 11.5-41.3% persist across all models. This propagation is pathway-specific (rates from 0.0% to 74.4% across mechanisms for GPT-5.4) and concentrated in browser-mediated data flows; under the matched alternative-value control, propagation persists (25.9% vs. 20.4%; the matched-pair CI spans zero, so the comparison neither distinguishes nor equates the two rates). A prompt-mitigation study across 3 models shows policy-violating propagation rates as low as 0.3% (98% relative reduction from baseline) while preserving 80.5% utility, but effectiveness varies across models, motivating data-flow-aware defenses beyond prompting. Code, traces, and the labeling pipeline are publicly available under MIT and CC BY 4.0.
Abstract: Multi-server MCP agents create an information-flow control (IFC) 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, exact-match 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 adds a matched alternative-value control (same tasks, distinct synthetic credentials); (3) completion-requires-secret (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 of 11.5-41.3% persist across all models. This propagation is pathway-specific (rates from 0.0% to 74.4% across mechanisms for GPT-5.4) and concentrated in browser-mediated data flows; under the matched alternative-value control, propagation persists (25.9% vs. 20.4%; the matched-pair CI spans zero, so the comparison neither distinguishes nor equates the two rates). A prompt-mitigation study across 3 models shows policy-violating propagation rates as low as 0.3% (98% relative reduction from baseline) while preserving 80.5% utility, but effectiveness varies across models, motivating data-flow-aware defenses beyond prompting. Code, traces, and the labeling pipeline are publicly available under MIT and CC BY 4.0.
This article has been indexed from cs.AI updates on arXiv.org
Read the original article:
MCPHunt: An Evaluation Framework for Cross-Boundary Data Propagation in Multi-Server MCP Agents