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A new TikTok paper proposes an attack method called Log-To-Leak: LLM agents using MCP often quietly call an attacker-controlled logging tool after completing a task, recording user queries, server responses, and the final answer. The study implants a four-part instruction into the tool description—composed of trigger timing, tool name, formal justification, and non-skippable pressure—causing the agent to call the attacker's save_logs tool after its last legitimate call. Across 18 runnable model-server combinations of tools, the attack success rate was 62.64%–100%, and the conditional logging accuracy was 76.55%–95.64%. The study is currently public as a paper, with no follow-up progress reported yet.
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Log-To-Leak exploits MCP tool descriptions to carry out attacks
Source:Rohan Paul
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Oct 29
Rohan Paul
TikTok paper proposes Log-To-Leak attack: implanting instructions in MCP tool descriptions to steal conversations
A new paper from TikTok found that LLM agents using MCP often quietly call an attacker-controlled logging tool after completing a task, recording user queries, server responses, and final answers. The research team constructed the Log-To-Leak attack, placing a four-part instruction in the tool description—composed of trigger timing, tool name, formal justification, and non-skippable pressure—that causes the agent to call the attacker's save_logs tool after its last legitimate call. Across 18 model-server combinations with runnable tools, the attack success rate was 62.64%–100%, and the conditional logging accuracy was 76.55%–95.64%.