Related papers: Toward Unbiased Multiple-Target Fuzzing with Path …
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…
Guided fuzzing has, in recent years, been able to uncover many new vulnerabilities in real-world software due to its fast input mutation strategies guided by path-coverage. However, most fuzzers are unable to achieve high coverage in deeper…
This paper presents a novel fuzzing framework, called MicroFuzz, specifically designed for Microservices. Mocking-Assisted Seed Execution, Distributed Tracing, Seed Refresh and Pipeline Parallelism approaches are adopted to address the…
Hardware Fuzzing emerged as one of the crucial techniques for finding security flaws in modern hardware designs by testing a wide range of input scenarios. One of the main challenges is creating high-quality input seeds that maximize…
The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored.…
Greybox fuzzing is the de-facto standard to discover bugs during development. Fuzzers execute many inputs to maximize the amount of reached code. Recently, Directed Greybox Fuzzers (DGFs) propose an alternative strategy that goes beyond…
Seed scheduling is a prominent factor in determining the yields of hybrid fuzzing. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. However, such heuristics are not generalizable as there…
The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their…
Directed greybox fuzzing (DGF) aims to efficiently trigger bugs at specific target locations by prioritizing seeds whose execution paths are more likely to reach the targets. However, existing DGF approaches suffer from imprecise potential…
Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for targeted detection grows, directed grey-box fuzzing (DGF) has…
Grey-box fuzzers such as American Fuzzy Lop (AFL) are popular tools for finding bugs and potential vulnerabilities in programs. While these fuzzers have been able to find vulnerabilities in many widely used programs, they are not efficient;…
Binary-only fuzzing often struggles with achieving thorough code coverage and uncovering hidden vulnerabilities due to limited insight into a program's internal dataflows. Traditional grey-box fuzzers guide test case generation primarily…
Fuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs/binaries. However,existing corpora usually do not capture…
Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws. Grey-box fuzzing, the most popular…
4G and 5G represent the current cellular communication standards utilized daily by billions of users for various applications. Consequently, ensuring the security of 4G and 5G network implementations is critically important. This paper…
Fuzz testing to find semantic control vulnerabilities is an essential activity to evaluate the robustness of autonomous driving (AD) software. Whilst there is a preponderance of disparate fuzzing tools that target different parts of the…
Coverage-guided fuzzing's aggressive, high-volume testing has helped reveal tens of thousands of software security flaws. While executing billions of test cases mandates fast code coverage tracing, the nature of binary-only targets leads to…
Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific…
Seed explosion is a fundamental problem in fuzzing seed scheduling, where a fuzzer maintains a huge corpus and fails to choose promising seeds. Existing works focus on seed prioritization but still suffer from seed explosion since corpus…
Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific assumptions, collaborative fuzzing relaxes assumptions on…