Related papers: An LLM-based Quantitative Framework for Evaluating…
This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the…
The prevalence of malicious packages in open-source repositories, such as PyPI, poses a critical threat to the software supply chain. While Large Language Models (LLMs) have emerged as a promising tool for automated security tasks, their…
The growing trend of vulnerability issues in software development as a result of a large dependence on open-source projects has received considerable attention recently. This paper investigates the effectiveness of Large Language Models…
Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and external packages required for a repository to successfully…
Open source software (OSS) is integral to modern product development, and any vulnerability within it potentially compromises numerous products. While developers strive to apply security patches, pinpointing these patches among extensive…
The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high…
Software vulnerabilities have a large negative impact on the software systems that we depend on daily. Reports on software vulnerabilities always paint a grim picture, with some reports showing that 83% of organizations depend on vulnerable…
As large language models (LLMs) become increasingly capable, it is prudent to assess whether safety measures remain effective even if LLMs intentionally try to bypass them. Previous work introduced control evaluations, an adversarial…
The sources of reliable, code-level information about vulnerabilities that affect open-source software (OSS) are scarce, which hinders a broad adoption of advanced tools that provide code-level detection and assessment of vulnerable OSS…
Evaluating Large Language Models (LLMs) for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we…
While automated vulnerability detection techniques have made promising progress in detecting security vulnerabilities, their scalability and applicability remain challenging. The remarkable performance of Large Language Models (LLMs), such…
Software vulnerabilities continue to be ubiquitous, even in the era of AI-powered code assistants, advanced static analysis tools, and the adoption of extensive testing frameworks. It has become apparent that we must not simply prevent…
The software product is a source of cyber-attacks that target organizations by using their software supply chain as a distribution vector. As the reliance of software projects on open-source or proprietary modules is increasing drastically,…
Using open-source dependencies is essential in modern software development. However, this practice implies significant trust in third-party code, while there is little support for developers to assess this trust. As a consequence, attacks…
Security vulnerabilities are increasingly prevalent in modern software and they are widely consequential to our society. Various approaches to defending against these vulnerabilities have been proposed, among which those leveraging deep…
The prevalent usage of open-source software (OSS) has led to an increased interest in resolving potential third-party security risks by fixing common vulnerabilities and exposures (CVEs). However, even with automated code analysis tools in…
Current software supply chains heavily rely on open-source packages hosted in public repositories. Given the popularity of ecosystems like npm and PyPI, malicious users started to spread malware by publishing open-source packages containing…
The democratization of open-source Large Language Models (LLMs) allows users to fine-tune and deploy models on local infrastructure but exposes them to a First Mile deployment landscape. Unlike black-box API consumption, the reliability of…
Language-based ecosystems (LBE), i.e., software ecosystems based on a single programming language, are very common. Examples include the npm ecosystem for JavaScript, and PyPI for Python. These environments encourage code reuse between…
The integration of large language models (LLMs) into cyber security applications presents both opportunities and critical safety risks. We introduce CyberLLMInstruct, a dataset of 54,928 pseudo-malicious instruction-response pairs spanning…