New 'Log-To-Leak' Attack Exploits LLM Agents via Prompt Injection

A new research paper titled "Log-To-Leak" details a prompt injection attack targeting LLM agents that use the Model Context Protocol (MCP) for tool access. This attack, called Log-To-Leak, exploits the natural-language metadata used to describe tools, enabling an attacker to exfiltrate sensitive information like user queries and server responses after legitimate tool calls. The research demonstrates high success rates for the attack across various MCP servers and LLM agents, even when defenses are in place, highlighting the need for host-controlled authorization for secondary tool calls. AI

IMPACT Highlights critical security vulnerabilities in LLM agent architectures, potentially impacting the safe deployment of AI systems that interact with external tools.

RANK_REASON Research paper detailing a novel attack vector on LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• GitHub MCP
• GPT-4o
• LLM agents
• Log-To-Leak
• Model Context Protocol

• paper
• safety

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