The paper "A Review of IoT Firmware Vulnerabilities and Auditing Techniques" by Taimur Bakhshi, Bogdan Ghita, and Ievgeniia Kuzminykh provides a comprehensive overview of IoT firmware vulnerabilities and auditing techniques. The authors highlight the growing cyber security threats targeting IoT-enabled platforms, particularly device firmware vulnerabilities, which are often overlooked during development and deployment. The paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective, dividing the IoT ecosystem into eight categories: system properties, access controls, hardware and software reuse, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. It discusses the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities and reviews state-of-the-art IoT firmware architectures and evaluation platforms. The paper also explores the feasibility and impact of machine learning and blockchain applications in securing IoT firmware. Finally, it concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate secure IoT development. The review aims to provide a detailed analysis of existing firmware problems, abstract vulnerability classifications, and discuss abstract vulnerability auditing solutions, along with the implications of future applications such as machine learning, deep learning, federated learning, and blockchain technology.The paper "A Review of IoT Firmware Vulnerabilities and Auditing Techniques" by Taimur Bakhshi, Bogdan Ghita, and Ievgeniia Kuzminykh provides a comprehensive overview of IoT firmware vulnerabilities and auditing techniques. The authors highlight the growing cyber security threats targeting IoT-enabled platforms, particularly device firmware vulnerabilities, which are often overlooked during development and deployment. The paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective, dividing the IoT ecosystem into eight categories: system properties, access controls, hardware and software reuse, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. It discusses the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities and reviews state-of-the-art IoT firmware architectures and evaluation platforms. The paper also explores the feasibility and impact of machine learning and blockchain applications in securing IoT firmware. Finally, it concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate secure IoT development. The review aims to provide a detailed analysis of existing firmware problems, abstract vulnerability classifications, and discuss abstract vulnerability auditing solutions, along with the implications of future applications such as machine learning, deep learning, federated learning, and blockchain technology.