MCP Safety: Measuring Useful Action across Tool Trust Boundaries

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.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23752412
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

MCP Safety: Measuring Useful Action across Tool Trust Boundaries

Pooria Arab
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
preprint

MCP Safety: Measuring Useful Action across Tool Trust Boundaries

Pooria Arab
preprint en

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.

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
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