VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
Katherine Swinea, Kshitiz Aryal, Lopamudra Praharaj, Maanak Gupta
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Key claim
VEXAIoT automates IoT vulnerability assessment with high success rates.
In plain English
IoT systems face significant security challenges due to their constrained hardware and insecure configurations. Current methods for vulnerability testing are often manual and limited in scope. This paper introduces VEXAIoT, an autonomous framework that uses AI agents to discover and exploit vulnerabilities in IoT environments. Builders might care because it automates the security testing process, achieving high success rates in identifying and exploiting vulnerabilities.
Introduces a novel multi-agent framework for IoT vulnerability exploitation using LLMs.
Demonstrates solid experimental results across multiple attack scenarios with clear metrics.
Deep reliability assessment
The methodology supports the claim that LLM-driven agents can automate IoT vulnerability assessment in controlled environments, but it may overclaim generalizability to real-world IoT systems with more dynamic and heterogeneous conditions.
Reproducibility
No open source code or dataset is mentioned in the paper.
Key figure
Figure 1 illustrates the VEXAIoT framework, showing the separation of vulnerability analysis and exploit execution into two independent agents.