MCP Safety: Measuring Useful Action across Tool Trust Boundaries
Year: 2026
Type: preprint
Abstract: MCP tools expose descriptions, returned content, and actions across trust boundaries. MCP Safety measures whether an agent completes a requested lookup without forbidden calls or literal canary disclosure. Its 40 synthetic tasks form 20 benign/malicious pairs across 6 attack categories. Deterministic replay scores safety and usefulness separately, then requires both for success. In 560 local scripted episodes, the privileged good control achieves 100.0 [83.9, 100.0] percent joint success in each stratum (95% Wilson intervals). The bad control completes the work but achieves 0.0 [0.0, 16.1] percent joint success. An additional 16000-episode simulated population sweep separates argument filtering from task-aware action filtering. At maximal simulated attack following, combining both filters preserves all requested work; either filter alone leaves failures. Blanket filtering loses legitimate work. These are diagnostic controls and a stipulated simulation, not learned-model results or evidence of deployment security.
Author: Pooria Arab
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7224377331