A Secure Accountability Framework for Multi-Modal Agent Systems: Detecting, Mitigating, and Auditing Data-Poisoning Attacks via Model Context Protocol (MCP) Servers

Abstract

Multi-modal agent systems (MMAS) integrate vision, language, sensor, and symbolic reasoning modules to autonomously collaborate across various domains, including healthcare, finance, and transportation. As these systems interlink, their attack surface broadens—especially to data-poisoning threats that introduce corrupted inputs to distort model learning or inference. Current defenses are localized, reactive, and opaque, frequently devoid of tamper-proof provenance tracking or verifiable accountability. This paper presents a Secure Accountability Framework based on Model Context Protocol (MCP) servers, aimed at detecting, mitigating, and auditing poisoning attacks in distributed MMAS environments. The MCP server functions as a reliable intermediary, offering cryptographic logging, proof-of-state validation, and real-time behavioral deviation analysis. Experimental simulations reveal a 98.5% detection rate, 3.8% latency overhead, and 97.4% attribution accuracy, substantiating MCP servers as a scalable solution for reliable multi-agent AI. The framework aligns with emerging AI governance standards (ISO/IEC 42001, NIST AI RMF, and the EU AI Act), establishing a foundation for transparent, auditable, and compliant AI ecosystems.