Related papers: Socialz: Multi-Feature Social Fuzz Testing
Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, which reflect the unpredictability and complexity of real-world…
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…
Programming errors that degrade the performance of systems are widespread, yet there is little tool support for analyzing these bugs. We present a method based on differential performance analysis---we find inputs for which the performance…
Context: Exhaustive fuzzing of modern JavaScript engines is infeasible due to the vast number of program states and execution paths. Coverage-guided fuzzers waste effort on low-risk inputs, often ignoring vulnerability-triggering ones that…
Firmware serves as the critical interface between hardware and software in computing systems, making any bugs or vulnerabilities particularly dangerous as they can cause catastrophic system failures. While fuzzing is a promising approach…
PHP, a dominant scripting language in web development, powers a vast range of websites, from personal blogs to major platforms. While existing research primarily focuses on PHP application-level security issues like code injection, memory…
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…
Critical open source software systems undergo significant validation in the form of lengthy fuzz campaigns. The fuzz campaigns typically conduct a biased random search over the domain of program inputs, to find inputs which crash the…
Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses.…
Coverage guided fuzzing (CGF) is an effective testing technique which has detected hundreds of thousands of bugs from various software applications. It focuses on maximizing code coverage to reveal more bugs during fuzzing. However, a…
Large Language Model (LLM) Agents leverage the advanced reasoning capabilities of LLMs in real-world applications. To interface with an environment, these agents often rely on tools, such as web search or database APIs. As the agent…
Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmented set of test cases, it may discover actual faults that…
In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the…
As deductive verifiers mature, their potential user base is growing from the initial core developers to other users. To convince external users of the suitability of verifiers, these tools must run reliably out of the box, give meaningful…
Network protocols are the foundation of modern communication, yet their implementations often contain semantic vulnerabilities stemming from inadequate understanding of specification semantics. Existing gray-box and black-box testing…
Despite much recent interest in compiler randomized testing (fuzzing), the practical impact of fuzzer-found compiler bugs on real-world applications has barely been assessed. We present the first quantitative and qualitative study of the…
Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to…
Greybox fuzzing is one of the most useful and effective techniques for the bug detection in large scale application programs. It uses minimal amount of instrumentation. American Fuzzy Lop (AFL) is a popular coverage based evolutionary…
Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is…
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…