A Protocol Fuzzing Framework to Detect Remotely Exploitable Vulnerabilities in IoT Nodes

Firmware attacks pose significant security risks to IoT devices due to often insufficient security protection. Such attacks can result in data breaches, privacy violations, and operational disruptions. For example, attackers can alter the structure of packets (e.g., header, payload) to create malformed packets and send them to nodes in a victim network. These packets can then remotely trigger vulnerabilities (e.g., buffer overflows) in the firmware, potentially resulting in crashes or unexpected behavior.In this paper, we first investigate how firmware vulnerabilities (e.g., out-of-bounds errors) can be remotely exploited in IoT networks by analyzing bug reports from leading IoT platforms. We categorize these findings and introduce a protocol fuzzing framework that leverages the GPT-4 model to guide the generation of test cases in form of malformed packets. These packets are later sent to 6LoWPAN-based nodes to observe their behavior and identify potential security vulnerabilities. As a result, we discovered four new vulnerabilities, classified as improper input validation, which can be remotely exploited in the Contiki-NG operating system.