Model Context Protocol for Vision Systems: Audit, Security, and Protocol Extensions

2025-09-26

AI Summary

This work addresses systemic deficiencies in the Model Context Protocol (MCP) for vision systems—specifically, ambiguities in pattern semantics, poor interoperability, and inadequate runtime coordination—including inconsistent format specifications, undeclared coordinate conventions, absence of runtime validation, and reliance on unaudited bridging scripts. We conduct the first large-scale, protocol-level empirical audit of MCP. Our methodology introduces a nine-dimensional compositional fidelity annotation framework and an executable validator suite integrating format conformance checking, coordinate consistency verification, memory-bound detection, and security probing, enabling automated compliance assessment on controlled platforms. Auditing 91 open-source MCP services, we find that 78% exhibit schema format deviations, 24.6% contain spatial reference errors, and memory warnings are triggered at an average rate of 33.8 per 100 executions. This study delivers the first reproducible protocol quality benchmark and open-source toolchain for building trustworthy, modular vision workflows.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The Model Context Protocol (MCP) defines a schema bound execution model for agent-tool interaction, enabling modular computer vision workflows without retraining. To our knowledge, this is the first protocol level, deployment scale audit of MCP in vision systems, identifying systemic weaknesses in schema semantics, interoperability, and runtime coordination. We analyze 91 publicly registered vision centric MCP servers, annotated along nine dimensions of compositional fidelity, and develop an executable benchmark with validators to detect and categorize protocol violations. The audit reveals high prevalence of schema format divergence, missing runtime schema validation, undeclared coordinate conventions, and reliance on untracked bridging scripts. Validator based testing quantifies these failures, with schema format checks flagging misalignments in 78.0 percent of systems, coordinate convention checks detecting spatial reference errors in 24.6 percent, and memory scope checks issuing an average of 33.8 warnings per 100 executions. Security probes show that dynamic and multi agent workflows exhibit elevated risks of privilege escalation and untyped tool connections. The proposed benchmark and validator suite, implemented in a controlled testbed and to be released on GitHub, establishes a reproducible framework for measuring and improving the reliability and security of compositional vision workflows.

Problem

• Auditing Model Context Protocol for security flaws in vision systems
• Identifying schema and coordination weaknesses in vision workflows
• Developing benchmark to detect protocol violations in vision systems

Innovation

• Defines schema-bound execution model for agent-tool interaction
• Develops executable benchmark with validators for protocol violations
• Establishes reproducible framework for vision workflow reliability