Related papers: Logic Solver Guided Directed Fuzzing for Hardware …
As processor designs grow more complex, verification remains bottlenecked by slow software simulation and low-quality random test stimuli. Recent research has applied software fuzzers to hardware verification, but these rely on semantically…
Industrial Control Protocols (ICPs) are critical to the reliability and stability of industrial infrastructure, yet their security is fundamentally compromised by a specification-blindness bottleneck. Modern fuzzers, constrained by…
Software vulnerabilities are constantly being reported and exploited in software products, causing significant impacts on society. In recent years, the main approach to vulnerability detection, fuzzing, has been integrated into the…
Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering…
Database Management Systems (DBMSs) are vital components in modern data-driven systems. Their complexity often leads to logic bugs, which are implementation errors within the DBMSs that can lead to incorrect query results, data exposure,…
Direct kernel fuzzing is a targeted approach that focuses on specific areas of the kernel, effectively addressing the challenges of frequent updates and the inherent complexity of operating systems, which are critical infrastructure. This…
Bugs exist in hardware, such as CPU. Unlike software bugs, these hardware bugs need to be detected before deployment. Previous fuzzing work in CPU bug detection has several disadvantages, e.g., the length of RTL input instructions keeps…
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…
Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering…
Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment…
Internet of Things (IoT) devices offer convenience through web interfaces, web VPNs, and other web-based services, all relying on the HTTP protocol. However, these externally exposed HTTP services resent significant security risks. Although…
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…
Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that hardware fuzzing is a promising technique to efficiently…
Dynamic data flow analysis has been widely used to guide greybox fuzzing. However, traditional dynamic data flow analysis tends to go astray in the massive path tracking and requires to process a large volume of data, resulting in low…
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…
Firmware fuzzing has gained attention for identifying firmware bugs. However, current approaches often directly integrate fuzzing tools for general software. General software receives input as it encounters I/O functions, but firmware input…
Generation-based fuzzing produces appropriate test cases according to specifications of input grammars and semantic constraints to test systems and software. However, these specifications require significant manual effort to construct. This…
Large Language Models (LLMs) have gained widespread use in various applications due to their powerful capability to generate human-like text. However, prompt injection attacks, which involve overwriting a model's original instructions with…
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…
Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to…