IoT Firmware Security Auditing Using Automated Vulnerability Scanning

Abstract

The exponential growth of the Internet of Things (IoT) has led to an unprecedented proliferation of connected devices across consumer, industrial, and critical-infrastructure domains. Firmware—the embedded software that governs hardware behavior—is often overlooked yet constitutes a critical attack surface. Security flaws in firmware can enable large-scale botnets, persistent backdoors, data exfiltration, and unauthorized control of devices. Traditional manual auditing approaches are labor-intensive, error-prone, and struggle to keep pace with the rapid firmware release cycles adopted by vendors. In this manuscript, we present an automated vulnerability-scanning framework tailored for IoT firmware security auditing. Our pipeline integrates multi-stage analysis—firmware unpacking, static rule-based inspection, dynamic emulation, API fuzzing, and machine-aided correlation—into a cohesive workflow.

Leveraging tools such as Binwalk, QEMU, AFL, and custom YARA rule sets, the framework identifies memory corruption issues, insecure configurations, outdated libraries, hardcoded credentials, and protocol-level flaws. Evaluated on 50 firmware images spanning routers, IP cameras, smart home hubs, and wearable gateways, the prototype achieved a 92% detection rate for known vulnerabilities, uncovered 37 novel security flaws, and reduced manual audit effort by 85%. Detailed performance metrics, false-positive statistics, and vendor-verified patch outcomes are discussed. Our results demonstrate that automated scanning significantly enhances coverage, repeatability, and efficiency of firmware security assessments, offering a scalable solution for device manufacturers, security researchers, and regulatory bodies.