Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem

Understand

Large language models(LLMs) are increasingly integrated with external systems through the Model Context Protocol(MCP),which standardizes tool invocation and has rapidly become a backbone for LLM-powered applications.

• While this paradigm enhances functionality,it also introduces a fundamental security shift:LLMs transition from passive information processors to autonomous orchestrators of task-oriented toolchains,expanding the attack surface,elevating adversarial goals from manipulating single outputs to hijacking entire execution flows.
• In this paper,we identify and characterize a systematic privacy-leakage attack pattern,termed Parasitic Toolchain Attacks,instantiated as MCP Unintended Privacy Disclosure(MCP-UPD).
• These attacks require no direct victim interaction;instead,adversaries embed malicious instructions into external data sources that LLMs access during legitimate tasks.