用LLM代理自动挖IoT漏洞，成功率95%，两分钟一个攻击，省时省力。
论文提出VEXAIoT，一个基于LLM的多智能体框架，包含漏洞检测代理和攻击执行代理，用于自主发现和利用IoT环境中的漏洞。在IoTGoat和Metasploitable2的10个OWASP IoT攻击场景中测试，单次攻击成功率高达100%，平均执行时间低于2分钟。在总共260次攻击执行中，整体成功率为95.0%，其中IoTGoat环境94.5%，Metasploitable2环境96.7%。结果表明LLM驱动的代理可自动化IoT漏洞评估和渗透测试。
VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches.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