Measuring and Exploiting Implicit Trust in LLM Tool-Calling Pipelines
Researchers quantify how MCP's shared trust context across tool descriptions, results, and sampling messages lets attackers hijack LLM behaviour—a systemic design flaw in current agent tool-calling architectures.
Summary written by editorial AI · Source link below
arXiv:2609.18217v1 Announce Type: new Abstract: The Model Context Protocol (MCP) enables LLMs to invoke external tools, but every tool interaction exposes the model to attacker-controlled text through multiple input channels (tool descriptions, tool results, sampling messages) that share a single context window without privilege separation. In this paper, we present a framework to measure the trust profile of an arbitrary LLM based on a variety of payload framings sent through different channel
Editorial Analysis
Enterprises rushing to deploy MCP-based AI agents inherit an architectural weakness where any attacker-controlled input channel can compromise the entire tool-calling pipeline.
Conduct a trust-boundary audit of all MCP-connected LLM tool-calling pipelines, enforcing channel isolation and input validation before production use.
AI agent tool-calling architectures have a systemic trust weakness that could allow attackers to manipulate automated business processes.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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