Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference

Feng, Xiaotao; Sun, Ruoxi; Zhu, Xiaogang; Xue, Minghui; Wen, Sheng; Liu, Dongxi; Nepal, Surya; Xiang, Yang

doi:10.48550/arxiv.2105.05445

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Snipuzz (Feng et al 2021), also aims to fuzz test IoT devices with accompanying mobile applications. Unlike IoTFuzzer and DIANE, it additionally analyzes responses from the target device to enable feedback-driven fuzzing.…”

Section: Hardware-based Embedded Fuzzingmentioning

confidence: 99%

Embedded fuzzing: a review of challenges, tools, and solutions

et al. 2022

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Fuzzing has become one of the best-established methods to uncover software bugs. Meanwhile, the market of embedded systems, which binds the software execution tightly to the very hardware architecture, has grown at a steady pace, and that pace is anticipated to become yet more sustained in the near future. Embedded systems also benefit from fuzzing, but the innumerable existing architectures and hardware peripherals complicate the development of general and usable approaches, hence a plethora of tools have recently appeared. Here comes a stringent need for a systematic review in the area of fuzzing approaches for embedded systems, which we term “embedded fuzzing” for brevity. The inclusion criteria chosen in this article are semi-objective in their coverage of the most relevant publication venues as well as of our personal judgement. The review rests on a formal definition we develop to represent the realm of embedded fuzzing. It continues by discussing the approaches that satisfy the inclusion criteria, then defines the relevant elements of comparison and groups the approaches according to how the execution environment is served to the system under test. The resulting review produces a table with 42 entries, which in turn supports discussion suggesting vast room for future research due to the limitations noted.

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Section: Hardware-based Embedded Fuzzingmentioning

confidence: 99%

Embedded fuzzing: a review of challenges, tools, and solutions

et al. 2022

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“…The kernel also dispatches exception signals such as SIGSEGV to the corresponding process when the process crashes. fuzzing (Feng et al 2021;Chen et al 2018;. The main fuzzing function is implemented in the packet sender, which can send a mutating request to the target process (yellow arrow in Fig.…”

Section: Observation and Motivationmentioning

confidence: 99%

“…The shortcomings of black-box fuzzing have been discussed for a long time. Therefore , Snipuzz (Feng et al 2021) propose a way to infer the running traces by analyzing the response contents to overcome these limitations . Its effectiveness depends on the quality of message snippets which is contingent on how much information could be obtained from devices' responses.…”

Section: Fuzzing Embedded Devicesmentioning

confidence: 99%

NDFuzz: a non-intrusive coverage-guided fuzzing framework for virtualized network devices

Zhong¹,

You²,

Zou³

et al. 2022

Cybersecurity

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Network function virtualization provides programmable in-network middlewares by leveraging virtualization technologies and commodity hardware and has gained popularity among all mainstream network device manufacturers. Yet it is challenging to apply coverage-guided fuzzing, one of the state-of-the-art vulnerability discovery approaches, to those virtualized network devices, due to inevitable integrity protection adopted by those devices. In this paper, we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protection bypassing method, which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique. We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols, SNMP , DHCP and NTP , on nine popular virtualized network devices. NDFuzz obtains an average 36% coverage improvement in comparison with its black-box counterparts. NDFuzz discovers 2 0-Day vulnerabilities and 1 1-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them. All discovered vulnerabilities are confirmed by corresponding vendors.

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Systematic Review of Fuzzing in IoT: Evaluating Techniques, Vulnerabilities, and Research Gaps

Touqir,

Iradat,

Rakib

et al. 2024

Preprint

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As our dependence on the internet and digital platforms grows, the risk of cyber threats rises, making it essential to implement effective Measures to safeguard sensitive information through cybersecurity, ensure system integrity, and prevent unauthorized data access. Fuzz testing, commonly known as fuzzing, is a valuable for software testing as it uncovers vulnerabilities and defects in systems by introducing random data inputs, often leading to system crashes. In the Internet of Things domain, fuzzing is crucial for identifying vulnerabilities in networks, devices, and applications through automated tools that systematically inject malformed inputs into IoT systems. This research aims to comprehensively evaluate current fuzzing practices, emphasizing adaptive techniques tailored to IoT environments. A rigorous analysis of 30 recent academic articles was conducted to identify weaknesses, gaps, and challenges in existing approaches. The investigation revealed the need for novel fuzzing techniques that address firmware, hardware, and software vulnerabilities, as well as Denial of Service attacks in IoT systems. By exploring recent trends and identifying gaps and challenges, this research aims to advance IoT security, highlighting the need for improved fuzzing techniques and presenting future research directions to strengthen IoT cybersecurity.

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Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference

Cited by 3 publications

References 25 publications

Embedded fuzzing: a review of challenges, tools, and solutions

Embedded fuzzing: a review of challenges, tools, and solutions

NDFuzz: a non-intrusive coverage-guided fuzzing framework for virtualized network devices

Systematic Review of Fuzzing in IoT: Evaluating Techniques, Vulnerabilities, and Research Gaps

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