标题： 当开发者援助变成安全债务：对LLM编码代理中不安全行为的系统分析 显示英文标题

标题： When Developer Aid Becomes Security Debt: A Systematic Analysis of Insecure Behaviors in LLM Coding Agents

摘要： 基于大语言模型的编码代理正在软件开发中迅速部署，但其安全影响仍知之甚少。 这些代理虽然能够加速软件开发，但可能会无意中引入不安全的做法。 我们进行了首次对自主编码代理的系统安全评估，在93个真实世界的软件设置任务上，分析了五种最先进的模型（GPT-4o、GPT-4.1、Claude变体）超过12,000次操作。 我们的研究结果揭示了重大的安全问题：21%的代理轨迹包含不安全操作，不同模型在安全行为上表现出显著差异。 我们开发了一个高精度的检测系统，识别出四个主要漏洞类别，其中信息泄露（CWE-200）是最普遍的一种。 我们还评估了各种缓解策略，包括反馈机制和安全提醒，不同模型的效果各不相同。 GPT-4.1在安全意识方面表现出色，缓解成功率达到96.8%。 我们的工作提供了首个全面的编码代理安全评估框架，并强调了下一代基于大语言模型的编码代理需要具备安全意识的设计。 显示英文摘要

摘要： LLM-based coding agents are rapidly being deployed in software development, yet their security implications remain poorly understood. These agents, while capable of accelerating software development, may inadvertently introduce insecure practices. We conducted the first systematic security evaluation of autonomous coding agents, analyzing over 12,000 actions across five state-of-the-art models (GPT-4o, GPT-4.1, Claude variants) on 93 real-world software setup tasks. Our findings reveal significant security concerns: 21% of agent trajectories contained insecure actions, with models showing substantial variation in security behavior. We developed a high-precision detection system that identified four major vulnerability categories, with information exposure (CWE-200) being the most prevalent one. We also evaluated mitigation strategies including feedback mechanisms and security reminders with various effectiveness between models. GPT-4.1 demonstrated exceptional security awareness with 96.8% mitigation success. Our work provides the first comprehensive framework for evaluating coding agent security and highlights the need for security-aware design of next generation LLM-based coding agents.