发表机构
Department of Computer Science Tennessee Tech University Cookeville, TN, USA; School of Interdisciplinary Informatics University of Nebraska Omaha Omaha, NE, USA; 1 Department of Computer Science, Tennessee Tech University, TN, USA.; 2 School of Interdisciplinary Informatics, University of Nebraska Omaha, NE, USA.; 3 Dept. of Mathematics \& Computer Science, University of North Carolina at Pembroke, NC, USA.（; ; ; ; ）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对物联网系统漏洞，提出VEXAIoT自主多代理框架，利用大语言模型推理和进攻性安全工具，结合漏洞检测与攻击执行代理，在特定环境评估中取得高成功率，证明其可实现物联网漏洞评估及安全工作流程自动化。
AI 中文摘要
物联网系统由于硬件受限、固件过时和默认配置不安全而固有地容易受到攻击，因此需要可扩展和自适应的安全测试方法。虽然最近采用的大语言模型代理在渗透测试和夺旗（CTF）环境中显示出前景，但它们在物联网特定漏洞方面的应用仍未得到探索。本文提出了一个自主多代理框架，即使用人工智能代理进行漏洞利用（VEXAIoT），用于在物联网环境中使用基于大语言模型的推理和进攻性安全工具进行漏洞发现和利用。该框架结合了漏洞检测代理和攻击执行代理，以执行侦察、规划攻击序列并对易受攻击的物联网服务执行利用操作。在物联网山羊（IoTGoat）和可利用的（Metasploitable）环境中，针对映射到OWASP物联网漏洞的十个攻击场景对系统进行了评估。实验结果表明，攻击成功率高达100%，令牌开销低，大多数攻击的平均执行时间不到两分钟。在260次攻击执行中，VEXAIoT的总体成功率达到95.0%，其中在物联网山羊中成功率为94.5%，在可利用的2中成功率为96.7%。这些结果证明了大语言模型驱动的代理在受控环境中实现物联网漏洞评估和进攻性安全工作流程自动化的潜力。
英文摘要
Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments, their application to IoT specific vulnerabilities remains unexplored. This paper presents an autonomous multi-agent framework, referred to as Vulnerability EXploitation using AI Agents (VEXAIoT), for vulnerability discovery and exploitation in IoT environments using LLM-based reasoning and offensive security tools. The framework combines a vulnerability detection agent and an attack execution agent to perform reconnaissance, plan attack sequences, and execute exploits against vulnerable IoT services. The system is evaluated in IoTGoat and Metasploitable environments across ten attack scenarios mapped to OWASP IoT vulnerabilities. Experimental results show attack success rate of up to 100% with low token overhead and average execution times under two minutes for most attacks. Across 260 attack executions, VEXAIoT achieves a 95.0% overall success rate, including 94.5% success in IoTGoat and 96.7% success in Metasploitable2. These results demonstrate the potential for LLM-driven agents to automate IoT vulnerability assessment and offensive security workflows in controlled environments
