Related papers: Type-Centric Kotlin Compiler Fuzzing: Preserving T…
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…
Feature Selection (FS) is crucial for improving model interpretability, reducing complexity, and sometimes for enhancing accuracy. The recently introduced Tsetlin machine (TM) offers interpretable clause-based learning, but lacks…
Cryptographic protocols form the backbone of modern security systems, yet vulnerabilities persist within their implementations. Traditional testing techniques, including fuzzing, have struggled to effectively identify vulnerabilities in…
Over 70% of security vulnerabilities in critical software systems today result from memory safety violations. To address this challenge, fuzzing and static analysis are widely used automated methods to discover such vulnerabilities. Fuzzing…
Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet…
We propose a tool, called FuzzingDriver, to generate dictionary tokens for coverage-based greybox fuzzers (CGF) from the codebase of any target program. FuzzingDriver does not add any overhead to the fuzzing job as it is run beforehand. We…
Fuzzing is a security testing methodology effective in finding bugs. In a nutshell, a fuzzer sends multiple slightly malformed messages to the software under test, hoping for crashes or weird system behaviour. The methodology is relatively…
Static and dynamic computational graphs represent two distinct approaches to constructing deep learning frameworks. The former prioritizes compiler-based optimizations, while the latter focuses on programmability and user-friendliness. The…
Solidity compiler plays a key role in enabling the development of smart contract applications on Ethereum by governing the syntax of a domain-specific language called Solidity and performing compilation and optimization of Solidity code.…
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 is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor…
Automatic test generation typically aims to generate inputs that explore new paths in the program under test in order to find bugs. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely…
Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Representation (MLIR) to facilitate more efficient development. With…
Many advanced program analysis and verification methods are based on solving systems of Constrained Horn Clauses (CHC). Testing CHC solvers is very important, as correctness of their work determines whether bugs in the analyzed programs are…
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…
Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate…
Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of compiled DL models. A recent bug study shows that the…
Fuzzing is a widely used software security testing technique that is designed to identify vulnerabilities in systems by providing invalid or unexpected input. Continuous fuzzing systems like OSS-FUZZ have been successful in finding security…
Fuzzing is a highly-scalable software testing technique that uncovers bugs in a target program by executing it with mutated inputs. Over the life of a fuzzing campaign, the fuzzer accumulates inputs inducing new and interesting target…
Directed fuzzing is a dynamic testing technique that focuses exploration on specific, pre targeted program locations. Like other types of fuzzers, directed fuzzers are most effective when maximizing testing speed and precision. To this end,…