Sequence Coverage Directed Greybox Fuzzing

Liu, Hongliang; Zhang, Yini; Yu, Yue; Xie, Zhuosi; Jiang, Lin

doi:10.1109/icpc.2019.00044

Cited by 32 publications

(34 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modern approaches to fuzzing aim to perform specific tasks that are challenging for automatic test generation methods. Some examples are the work of Liang et al [54] applying directed greybox fuzz to cover different sequences of programs, Nilizadeh et al who apply differential fuzzing for side-channel analysis [55], Cerebro [56] that fuzzes the program's context to detect vulnerabilities, ContractFuzzer [57] that detects vulnerabilities on smart contracts or Dee-pHunter [58] and SeqFuzzer [59] that fuzz deep learning algorithms.…”

Section: Automated Test Generation and Fuzzersmentioning

confidence: 99%

Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection

Menéndez

Clark

2022

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

Section: Automated Test Generation and Fuzzersmentioning

confidence: 99%

Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection

Menéndez

Clark

2022

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…Directed fuzzers allow to redirect test execution to avoid repeating paths and cover all the codes, if necessary [15]. Most directed fuzzers are usually white-box fuzzers based on the symbolic execution and oriented to generate test inputs [69]. Dowser is an example of the directed fuzzer [70].…”

Section: A Fuzzing Taxonomymentioning

confidence: 99%

Fuzzing the Internet of Things: A Review on the Techniques and Challenges for Efficient Vulnerability Discovery in Embedded Systems

Eceiza

Flores

Iturbe

2021

IEEE Internet Things J.

View full text Add to dashboard Cite

With a growing number of embedded devices that create, transform, and send data autonomously at its core, the Internet of Things (IoT) is a reality in different sectors, such as manufacturing, healthcare, or transportation. With this expansion, the IoT is becoming more present in critical environments, where security is paramount. Infamous attacks, such as Mirai, have shown the insecurity of the devices that power the IoT, as well as the potential of such large-scale attacks. Therefore, it is important to secure these embedded systems that form the backbone of the IoT. However, the particular nature of these devices and their resource constraints mean that the most cost-effective manner of securing these devices is to secure them before they are deployed, by minimizing the number of vulnerabilities they ship. To this end, fuzzing has proved itself as a valuable technique for automated vulnerability finding, where specially crafted inputs are fed to programs in order to trigger vulnerabilities and crash the system. In this survey, we link the world of embedded IoT devices and fuzzing. For this end, we list the particularities of the embedded world as far as security is concerned, we perform a literature review on fuzzing techniques and proposals, studying their applicability to embedded IoT devices and, finally, we present future research directions by pointing out the gaps identified in the review.

show abstract

“…Coverage-based Greybox Fuzzing (CGF), such as AFL [1] and LIBFUZZER [14], leverages code coverage information in order to guide input generation toward new parts of the program under test (PUT), exploring as many program states as possible in the hope of triggering crashes. On the other hand, Directed Greybox Fuzzing (DGF) [25,28] aims to perform stress testing on pre-selected potentially vul-nerable target locations, with applications to different security contexts: (1) bug reproduction [25,28,42,61], (2) patch testing [25,51,59] or (3) static analysis report verification [31,49]. Depending on the application, target locations are originated from bug stack traces, patches or static analysis reports.…”

Section: Introductionmentioning

confidence: 99%

Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities

Nguyen¹,

Bardin²,

Bonichon³

et al. 2020

Preprint

View full text Add to dashboard Cite

Directed fuzzing focuses on automatically testing specific parts of the code by taking advantage of additional information such as (partial) bug stack trace, patches or risky operations. Key applications include bug reproduction, patch testing and static analysis report verification. Although directed fuzzing has received a lot of attention recently, hard-to-detect vulnerabilities such as Use-After-Free (UAF) are still not well addressed, more especially at the binary level. We propose UAFUZZ, the first (binary-level) directed greybox fuzzer dedicated to UAF bugs. The technique features a fuzzing engine tailored to UAF specifics, a lightweight code instrumentation and an efficient bug triage step. Experimental evaluation for bug reproduction on real cases demonstrates that UAFUZZ significantly outperforms state-of-the-art directed fuzzers in terms of fault detection rate, time to exposure and bug triaging. UAFUZZ has also been proven effective in patch testing, leading to the discovery of 20 new bugs in Perl, GPAC and GNU Patch (including a buggy patch) -all of them have been acknowledged and 14 have been fixed. Last but not least, we provide to the community the first fuzzing benchmark dedicated to UAF, built on both real codes and real bugs.

show abstract

Sequence Coverage Directed Greybox Fuzzing

Cited by 32 publications

References 19 publications

Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection

Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection

Fuzzing the Internet of Things: A Review on the Techniques and Challenges for Efficient Vulnerability Discovery in Embedded Systems

Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities

Contact Info

Product

Resources

About