[论文] [论文] A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem

论文概要

研究领域: 安全
作者: Laizhen Li, Xuan Wang, Peicheng Zhao, Juanjuan Zhao, Kejiang Ye et al.
发布时间: 2026-09-22
arXiv: 2609.26761

中文摘要

使用模型上下文协议（MCP）的智能体依赖语义匹配从第三方服务器选工具，经攻击者控制的元数据与输出暴露语义供应链风险。我们提出 A2M（吸引-操纵）——两阶段黑盒框架劫持 MCP 智能体："吸引"阶段优化工具元数据以提高被调概率；"操纵"阶段利用执行轨迹精化对抗性工具返回，把智能体引向攻击者期望结果。LiveMCPBench 上，对 GLM-4.6 优化并评测的直接攻击：四场景宏平均恶意工具调用率 93.6%；"认知拒绝服务"下加权 token 成本达良性基线 32.4 倍；信息窃取、环境完整性破坏、推理脱轨三类场景平均攻击成功率 74.4%。不经重优化迁移到另四个模型，对应宏平均 63.6%、2.7×、24.5%。这些发现促使 MCP 生态加强工具审核与运行时隔离。代码开源于 https://github.com/Lilaizhen/A2M。

原文摘要

Agents using the Model Context Protocol (MCP) rely on semantic matching to select tools from third-party servers, exposing a semantic supply-chain risk through attacker-controlled metadata and outputs. We introduce A2M (Attraction-to-Manipulation), a two-stage black-box framework for hijacking MCP agents. The Attraction phase optimizes tool metadata to increase invocation probability; the Manipulation phase uses execution traces to refine adversarial tool returns that steer agents toward attacker-desired outcomes. On LiveMCPBench, direct attacks optimized and evaluated on GLM-4.6 achieve a macro-average malicious tool invocation rate of 93.6% across four scenarios, increase weighted token costs to 32.4\(\times\) the benign baseline under Cognitive Denial of Service, and attain a mean attack success rate of 74.4% across Information Exfiltration, Environment Integrity Compromise, and Reasoning Derailment. Transfer to four other models without re-optimization yields corresponding macro-averages of 63.6%, 2.7\(\times\), and 24.5%. These findings motivate stronger tool vetting and runtime isolation in MCP ecosystems. Code is publicly available at https://github.com/Lilaizhen/A2M.

自动采集于 2026-09-24

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