PDFuzzerGen: Policy-Driven Black-Box Fuzzer Generation for Smart Devices. (27th April 2022)

• Record Type: Journal Article
• Authors: Cheng, Yixuan; Fan, Wenqing; Huang, Wei; Yu, Gaoqing; Han, Yu; Dong, Hang; Liu, Wen
• Other Names: Zhang Tao Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-04-27
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/9788219
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Black-box fuzzing is a testing technique to find both known and unknown vulnerabilities in software. When applying black-box fuzzing to smart devices, the main idea is to take a smart device as a black box and provide random input through a network-based interface, such as a Web interface. Due to the diversity of Web interface implementations and complex data format, a blind mutation of the message makes the message unable to pass the verification of the device component. Therefore, each Web interface needs a unique fuzzer, which precisely defines a message format of the target interface, a state maintenance method, the field positions to be mutated, and a specific input mutation method. At the time of writing, a fuzzer is completely developed by a security engineer. To save human labor, we present PDFuzzerGen, a tool to automatically synthesize complex black-box fuzzers for smart devices. PDFuzzerGen generates multiple fuzzing policies by analyzing raw messages and then synthesizes fuzzers based on policies. PDFuzzerGen requires no human intervention and can be applied to a wide range of smart devices. Furthermore, the generated fuzzers can expose bugs and flaws that rest deep in smart devices. PDFuzzerGen was evaluated to generate fuzzers for 19 different smart devices from 6 vendors. It has found 14 previously unknown vulnerabilities, 5 of which were confirmed and disclosed by the China National Vulnerability Database (CNVD) and 2 of which were confirmed and disclosed by Common Vulnerabilities and Exposures (CVE). The generated fuzzers outperform some manually crafted fuzzers on a few metrics, including the vulnerability detection rate and time cost of a newly developed fuzzer, which demonstrates the effectiveness and efficiency of PDFuzzerGen.