WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
also in 1 more
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 23 of its 26 references that are in the map. First posted on arXiv 25 Aug 2026.
Builds on
23 of 26 references.
Closest ancestors · 12
Same direction, newest first
- 2026AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations11 Mar
- 2025ObliInjection: Order-Oblivious Prompt Injection Attack to LLM Agents with Multi-source Data10 Dec
- 2025ToolTweak: An Attack on Tool Selection in LLM-based Agents2 Oct
- 2025ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents26 Sep
- 2025VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents3 Jun
- 2025MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures -- A Comprehensive Framework24 May
- 2025WebInject: Prompt Injection Attack to Web Agents16 May
- 2025Les Dissonances: Cross-Tool Harvesting and Polluting in Pool-of-Tools Empowered LLM Agents4 Apr
- 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents17 Mar
- 2025G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems16 Feb
- 2025MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents7 Feb
- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems8 Mar
Other references · 11
- 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
- 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
- 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 210
- 2023Poisoning Retrieval Corpora by Injecting Adversarial Passagescited by 159
- 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Serverscited by 109
- 2025MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Serverscited by 96
- 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 71
- 2025ACE: A Security Architecture for LLM-Integrated App Systemscited by 55
- 2025MCIP: Protecting MCP Safety via Model Contextual Integrity Protocolcited by 34
- 2025TopicAttack: An Indirect Prompt Injection Attack via Topic Transitioncited by 17
- 2026Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systemscited by 16
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
- 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
- 2026OpenClaw PRISM: A Zero-Fork, Defense-in-Depth Runtime Security Layer for Tool-Augmented LLM Agents
- 2026ToolFence: Fine-Grained Authorization for Secure Tool-Using LLM Agents
- 2026Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents
- 2026TraceGrant: A Contract-Governed Security Framework for the Task-Effect Lifecycle of Networked LLM Agents
- 2026MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
- 2026ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection