MCP智能体互通信被指存在信任缺口，有害指令可在内部代理间扩散
MCP for agent-to-agent comms may be the riskiest protocol you've never heard of
过去五个月，Google及其他四家机构承认AI智能体漏洞，攻击者可借目标网络内一个智能体向其他内部智能体扩散有害指令。独立研究者Syed Anas Mohiuddin的概念验证攻击利用MCP（Model Context Protocol）的信任缺口，测试对象包括Google、JP Morgan Chase、Weviate、Rapid7、法国政府部际数字局和美国联邦政府的智能体。
The adoption of AI agents in millions of organizations is creating new opportunities for attackers to make them take malicious actions, such as exfiltrating database contents and sensitive business and personal information.
In the past five months, Google and four other organizations—with little in common except for their use of AI agents—have acknowledged vulnerabilities that exploit one agent inside a targeted network to spread harmful instructions to other internal agents. The technique is a special form of prompt injection that targets not the LLM but a particular agent, such as one for translation or data analysis. Guardrails inside such agents, if they exist at all, are often lax and will send the instructions to other agents down the chain. Because the latter agent explicitly trusts the first one, it follows the directions.
Unexpected and hard to mitigate
Independent researcher Syed Anas Mohiuddin tested agents from organizations including Google, JP Morgan Chase, Weviate, Rapid7, the French government's interministerial digital directorate, and the US federal government. His proof-of-concept attacks exploit trust gaps in MCP, short for Model Context Protocol. The standard is one way AI apps and agents communicate with each other inside an internal network. The illustration below shows a simplified MCP in action.
来源：Ars Technica · AI · arstechnica.com