Related papers: ISC4DGF: Enhancing Directed Grey-box Fuzzing with …

We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performs…

Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL libraries and frameworks. However, existing work largely overlooked…

Information leakage is a class of error that can lead to severe consequences. However unlike other errors, it is rarely explicitly considered during the software testing process. LeakFuzzer advances the state of the art by using a…

Federated Learning (FL) offers collaborative model training without data sharing but is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. Existing countermeasures, primarily based on anomaly…

Fuzzing is a powerful software testing technique renowned for its effectiveness in identifying software vulnerabilities. Traditional fuzzing evaluations typically focus on overall fuzzer performance across a set of target programs, yet few…

Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar…

Fuzz testing (fuzzing) is a well-known method for exposing bugs/vulnerabilities in software systems. Popular fuzzers, such as AFL, use a biased random search over the domain of program inputs, where 100s or 1000s of inputs (test cases) are…

Bugs in operating system kernels can affect billions of devices and users all over the world. As a result, a large body of research has been focused on kernel fuzzing, i.e., automatically generating syscall (system call) sequences to detect…

Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions…

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning…

Software fuzzing is a strong testing technique that has become the de facto approach for automated software testing and software vulnerability detection in the industry. The random nature of fuzzing makes monitoring and understanding the…

Software-defined networks (SDN) enable flexible and effective communication systems that are managed by centralized software controllers. However, such a controller can undermine the underlying communication network of an SDN-based system…

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the…

Software model checking is a verification technique which is widely used for checking temporal properties of software systems. Even though it is a property verification technique, its common usage in practice is in "bug finding", that is,…

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…

Recent research has shown that hardware fuzzers can effectively detect security vulnerabilities in modern processors. However, existing hardware fuzzers do not fuzz well the hard-to-reach design spaces. Consequently, these fuzzers cannot…

Vulnerable software represents a tremendous threat to modern information systems. Vulnerabilities in widespread applications may be used to spread malware, steal money and conduct target attacks. To address this problem, developers and…

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…

Information granules have been considered to be the fundamental constructs of Granular Computing (GrC). As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information…

Recently, many Deep Learning fuzzers have been proposed for testing of DL libraries. However, they either perform unguided input generation (e.g., not considering the relationship between API arguments when generating inputs) or only…