Related papers: deepSURF: Detecting Memory Safety Vulnerabilities …

Each year, software vulnerabilities are discovered, which pose significant risks of exploitation and system compromise. We present a convolutional neural network model that can successfully identify bugs in C code. We trained our model…

Automating software vulnerability detection (SVD) remains a critical challenge in an era of increasingly complex and interdependent software systems. Despite significant advances in Large Language Models (LLMs) for code analysis, prevailing…

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…

With the rapid advancements in Natural Language Processing (NLP), large language models (LLMs) like GPT-4 have gained significant traction in diverse applications, including security vulnerability scanning. This paper investigates the…

The widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage. These attacks pose significant risks to high-reliability applications,…

The increasing complexity of modern processor and IP designs presents significant challenges in identifying and mitigating hardware flaws early in the IC design cycle. Traditional hardware fuzzing techniques, inspired by software testing,…

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…

Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustness to explicit noise (e.g., document semantics) but overlooks implicit noise (spurious features).…

WebAssembly binaries are often compiled from memory-unsafe languages, such as C and C++. Because of WebAssembly's linear memory and missing protection features, e.g., stack canaries, source-level memory vulnerabilities are exploitable in…

Fuzz testing is one of the most effective techniques for detecting bugs and vulnerabilities in software. However, as the basis of fuzz testing, automated heuristics often fail to uncover deep or complex vulnerabilities. As a result, the…

Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and…

A growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, which serve as the foundations for building, training, and running DL models. Prior work on fuzzing DL…

Compilers constitute the foundational root-of-trust in software supply chains; however, their immense complexity inevitably conceals critical defects. Recent research has attempted to leverage historical bugs to design new mutation…

Rust is a strong contender for a memory-safe alternative to C as a "systems" language, but porting the vast amount of existing C code to Rust remains daunting. In this paper, we evaluate the potential of large language models (LLMs) to…

Since the advent of AFL, the use of mutational, feedback directed, grey-box fuzzers has become critical in the automated detection of security vulnerabilities. A great deal of research currently goes into their optimisation, including…

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…

Fuzz testing to find semantic control vulnerabilities is an essential activity to evaluate the robustness of autonomous driving (AD) software. Whilst there is a preponderance of disparate fuzzing tools that target different parts of the…

Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly…

Testing-based methodologies like fuzzing are able to analyze complex software which is not amenable to traditional formal approaches like verification, model checking, and abstract interpretation. Despite enormous success at exposing…

Rust is a modern systems programming language that ensures memory safety by enforcing ownership and borrowing rules at compile time. While the unsafe keyword allows programmers to bypass these restrictions, it introduces significant risks.…