LLM agents vulnerable to unauthorized tool access; new proxy offers 0% UIR

A new research paper highlights a critical security vulnerability in large language models (LLMs) when they function as autonomous agents. The study found that models like Qwen 2.5 7B, Llama-3.1:8b, and Claude Haiku 3.5 frequently select unauthorized tools, even when explicitly instructed not to. Researchers developed a governed MCP proxy system that enforces access control at both tool discovery and invocation stages, successfully reducing the unauthorized invocation rate to 0% across all tested models with minimal overhead. AI

IMPACT Highlights critical security flaws in LLM agent architectures, necessitating architectural enforcement over prompt-based controls for tool access.

RANK_REASON Research paper published on arXiv detailing a security vulnerability in LLM agents and proposing a solution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Claude Haiku 3.5
• DagsHub
• Gotit.pub
• Hugging Face
• Llama-3.1:8b
• MCP proxy
• Qwen 2.5 7B
• Rohith Uppala
• ScienceCast

• paper
• safety

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