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New MCP proxy enforces LLM tool access control architecturally

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new architectural enforcement method called the MCP proxy to control Large Language Model (LLM) access to tools. This proxy addresses a critical security gap where LLMs can select unauthorized tools even when explicitly instructed not to. By removing unauthorized tools from the model's context during discovery and adding a second check at invocation, the MCP proxy effectively eliminates unauthorized tool usage across multiple LLM models and adversarial scenarios. The study demonstrates that architectural enforcement, rather than prompt-based restrictions, is essential for secure tool access control in deployed agentic systems. AI

IMPACT This research introduces a robust architectural solution for LLM tool access control, crucial for the safe deployment of agentic AI systems.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MCP proxy enforces LLM tool access control architecturally

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The cluster contains an academic paper detailing a new method for LLM security. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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144 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rohith Uppala ·

    Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control

    Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when unauthorized tools are visible in an agent's context, models select them in adversarial scenarios -- even when explicitly instructe…

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