Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the article introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed.

With the growing prevalence of the Internet of Things (IoT), related security threats have kept pace. The need to dynamically detect vulnerabilities in IoT devices cannot be overstated. In this work, we present FIRM-COV, the first high coverage-oriented greybox fuzzer for IoT firmware. FIRM-COV leverages newly optimized process emulation by targeting IoT programs and mining real-world vulnerabilities. FIRM-COV focuses on solving problems of IoT fuzzing based on empirical analyses, using the required structured input, the inaccuracy and instability of emulation, and the required high code coverage. By optimizing the existing emulation technique, FIRM-COV always maintains a stable state and achieves high accuracy when detecting vulnerabilities. We also implement a dictionary generation algorithm to provide structured input values and synergy scheduling to achieve high coverage and throughput. We compare FIRM-COV with other IoT fuzzing frameworks for eight real-world IoT devices. As a result, FIRM-COV achieves the highest coverage and throughput, finding the fastest and most 1-day vulnerabilities with almost no false-positives. It also found two 0-day vulnerabilities in real-world IoT devices within 24 h.

Recently, software testing has become a significant component of information security. The most reliable technique for automated software testing is a fuzzing tool that feeds programs with random test-input and detects software vulnerabilities that are critical to security. Similarly, symbolic execution has gained the most attention as an efficient testing tool for producing smart test-inputs and discovering hard-to-reach bugs using search-based heuristics and compositional approaches. The combination of fuzzing and symbolic execution makes software testing more efficient by mitigating the limitations in each other. Although several studies have been conducted on hybrid fuzzing in recent years, a comprehensive and consistent review of hybrid fuzzing techniques has not been explored. To add coherence to the extensive literature on hybrid fuzzing and to make it reach a large audience, this study provides an overview of key concepts along with the taxonomy of existing hybrid fuzzing tools, problems, and solutions that have been developed in this sphere. It also includes evaluations of the proposed approaches and a number of suggestions for the development of hybrid fuzzing in the future.

The supply of IoT devices is increasing year by year. Even in industries that demand sophistication, such as unmanned driving, construction, and robotics industry, IoT devices are being utilized. However, the security of IoT devices is lagging behind this development due to their diverse types and challenging firmware execution environments. The existing methods, such as direct device connectivity or partial emulation, are used to solve this. However, full system emulation is better suited for the large-scale analysis, because it can test many firmwares without requiring devices. Therefore, recent studies have integrated emulation and software testing techniques such as fuzzing, but they are still unsuitable for testing various firmware and inefficient. In this poster, we propose FirmColic, which combines fuzzing with concolic execution to mitigate these limitations. FirmColic is a type of augmented process emulation, which improves the effectiveness of fuzzing using keyword extraction based on concolic execution. Also, we apply five arbitration techniques in an augmented process emulation environment for the high success rates of the emulation. We prove that FirmColic has faster detection, more crash detection, and a higher code coverage than the previous studies.

Greybox Fuzzing is the most reliable and essentially powerful technique for automated software testing. Notwithstanding, a majority of greybox fuzzers are not effective in directed fuzzing, for example, towards complicated patches, as well as towards suspicious and critical sites. To overcome these limitations of greybox fuzzers, Directed Greybox Fuzzing (DGF) approaches were recently proposed. Current DGFs are powerful and efficient approaches that can compete with Coverage-Based Fuzzers. Nevertheless, DGFs neglect to accomplish stability between usefulness and proficiency, and random mutations make it hard to handle complex paths. To alleviate this problem, we propose an innovative methodology, a target-oriented hybrid fuzzing tool that utilizes a fuzzer and dynamic symbolic execution (also referred to as a concolic execution) engine. Our proposed method aims to generate inputs that can quickly reach the target sites in each sequence and trigger potential hard-to-reach vulnerabilities in the program binary. Specifically, to dive deep into the target binary, we designed a proposed technique named BugMiner, and to demonstrate the capability of our implementation, we evaluated it comprehensively on bug hunting and crash reproduction. Evaluation results showed that our proposed implementation could not only trigger hard-to-reach bugs 3.1, 4.3, 2.9, 2.0, 1.8, and 1.9 times faster than Hawkeye, AFLGo, AFL, AFLFast, QSYM, and ParmeSan respectively but also scale to several real-world programs.