WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents

📅 2026-08-24
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
本文提出WebMCP-Phalanx架构,通过双层代理运行时机制解决浏览器集成LLM代理中的信任边界问题,增强安全性和防止语义提示注入。
📝 Abstract
The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent execution into a browser security model centered on the Same-Origin Policy (SOP) leaves insufficient provenance and lifecycle guarantees for agent-accessible tools, creating three risks: subject-attribution spoofing, uncontrolled tool lifecycles, and semantic prompt injection. We propose WebMCP-Phalanx, a dual-layer agent runtime architecture. Its first layer provides a browser-native trust anchor that binds each tool to its registering principal through cryptographically protected capability credentials and propagates provenance labels throughout the tool lifecycle. Its second layer separates semantic inspection from privileged tool use. A Quarantine Agent (Q-LLM), without tool invocation authority, inspects tool metadata, outputs, and page-supplied content for prompt injection. Validated content is then forwarded to a Privileged Agent (P-LLM) for execution, while the Q-LLM's internal state remains hidden from page scripts. Empirical evaluation shows that the browser-native ownership mechanism reduces revocation and overwrite attack success from 100\% to 0\%. The dual-agent runtime blocks all 80 prompt-injection attempts embedded in tool descriptions and limits tool-return attacks to 2 successful cases out of 80. Across experiments, task utility remains statistically indistinguishable from the no-attack baseline. Under a white-box adaptive attacker, however, description-based filtering can be bypassed through malicious tool names invoked before inspection. This finding motivates a call-timing gate that delays tool invocation until all agent-visible tool metadata has been validated.
Problem

Research questions and friction points this paper is trying to address.

WebMCP
Same-Origin Policy
semantic prompt injection
Innovation

Methods, ideas, or system contributions that make the work stand out.

Dual-layer Agent Runtime
Cryptographically Protected Capability Credentials
Quarantine Agent (Q-LLM)
Privileged Agent (P-LLM)
Semantic Prompt Injection
L
Lin-Fa Lee
Department of Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Y
YI-YU Chang
Department of Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Kuo-Hui Yeh
Kuo-Hui Yeh
National Yang Ming Chiao Tung University
SecurityPrivacy