Related papers: InsightQL: Advancing Human-Assisted Fuzzing with a…

Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering…

Fuzzing is one of the key techniques for evaluating the robustness of programs against attacks. Fuzzing has to be effective in producing inputs that cover functionality and find vulnerabilities. But it also has to be efficient in producing…

Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to approaches for solving individual path constraints. However, they have difficulty solving path constraints that involve deeply…

Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in…

Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particularly methods utilizing large language models, has become…

While fuzzing is widely accepted as an efficient program testing technique, it is still unclear how to measure the comparative quality of different fuzzers. The current de facto quality metrics are edge coverage and the number of discovered…

Implementations of network protocols are often prone to vulnerabilities caused by developers' mistakes when accessing memory regions and dealing with arithmetic operations. Finding practical approaches for checking the security of network…

Detecting bugs in Deep Learning (DL) libraries (e.g., TensorFlow/PyTorch) is critical for almost all downstream DL systems in ensuring effectiveness/safety for end users. Meanwhile, traditional fuzzing techniques can be hardly effective for…

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging due to the need for satisfying both language…

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities.…

Protocol fuzzing is a scalable and cost-effective technique for identifying security vulnerabilities in deployed Internet of Things devices. During their operational phase, IoT devices often run lightweight servers to handle user…

Fuzzing has proven to be a fundamental technique to automated software testing but also a costly one. With the increased adoption of CI/CD practices in software development, a natural question to ask is `What are the best ways to integrate…

Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and…

Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. Moreover, when the application fails on a large benchmark, the debugging process may be tedious and…

As the complexity of logic designs increase, new avenues for testing digital hardware becomes necessary. Fuzz Testing (fuzzing) has recently received attention as a potential candidate for input vector generation on hardware designs. Using…

Finding software vulnerabilities in concurrent programs is a challenging task due to the size of the state-space exploration, as the number of interleavings grows exponentially with the number of program threads and statements. We propose…

In modern software development, vulnerability detection is crucial due to the inevitability of bugs and vulnerabilities in complex software systems. Effective detection and elimination of these vulnerabilities during the testing phase are…

The emerging data-intensive applications are increasingly dependent on data-intensive scalable computing (DISC) systems, such as Apache Spark, to process large data. Despite their popularity, DISC applications are hard to test. In recent…

Emulation-based fuzzers enable testing binaries without source code, and facilitate testing embedded applications where automated execution on the target hardware architecture is difficult and slow. The instrumentation techniques added to…

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault…