QueryIPI: Query-agnostic Indirect Prompt Injection on Coding Agents

• URL: http://arxiv.org/abs/2510.23675v1
• Date: Mon, 27 Oct 2025 07:04:08 GMT
• Title: QueryIPI: Query-agnostic Indirect Prompt Injection on Coding Agents
• Authors: Yuchong Xie, Zesen Liu, Mingyu Luo, Zhixiang Zhang, Kaikai Zhang, Zongjie Li, Ping Chen, Shuai Wang, Dongdong She,
• Abstract summary: We present QueryIPI, the first query-agnostic IPI method for coding agents.<n>It refines malicious tool descriptions through an iterative, prompt-based process informed by the leaked internal prompt.<n>Experiments on five simulated agents show that QueryIPI achieves up to 87 percent success.
• Score: 13.098854359317523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern coding agents integrated into IDEs combine powerful tools and system-level actions, exposing a high-stakes attack surface. Existing Indirect Prompt Injection (IPI) studies focus mainly on query-specific behaviors, leading to unstable attacks with lower success rates. We identify a more severe, query-agnostic threat that remains effective across diverse user inputs. This challenge can be overcome by exploiting a common vulnerability: leakage of the agent's internal prompt, which turns the attack into a constrained white-box optimization problem. We present QueryIPI, the first query-agnostic IPI method for coding agents. QueryIPI refines malicious tool descriptions through an iterative, prompt-based process informed by the leaked internal prompt. Experiments on five simulated agents show that QueryIPI achieves up to 87 percent success, outperforming baselines, and the generated malicious descriptions also transfer to real-world systems, highlighting a practical security risk to modern LLM-based coding agents.

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