Large language model for vulnerability detection and repair: Literature review and the road ahead
The significant advancements in Large Language Models (LLMs) have resulted in their
widespread adoption across various tasks within Software Engineering (SE), including …
widespread adoption across various tasks within Software Engineering (SE), including …
Generative ai and large language models for cyber security: All insights you need
This paper provides a comprehensive review of the future of cybersecurity through
Generative AI and Large Language Models (LLMs). We explore LLM applications across …
Generative AI and Large Language Models (LLMs). We explore LLM applications across …
From llms to llm-based agents for software engineering: A survey of current, challenges and future
With the rise of large language models (LLMs), researchers are increasingly exploring their
applications in var ious vertical domains, such as software engineering. LLMs have …
applications in var ious vertical domains, such as software engineering. LLMs have …
International Scientific Report on the Safety of Advanced AI (Interim Report)
This is the interim publication of the first International Scientific Report on the Safety of
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …
Outside the comfort zone: Analysing llm capabilities in software vulnerability detection
The significant increase in software production driven by automation and faster development
lifecycles has resulted in a corresponding surge in software vulnerabilities. In parallel, the …
lifecycles has resulted in a corresponding surge in software vulnerabilities. In parallel, the …
[HTML][HTML] A comprehensive review and assessment of cybersecurity vulnerability detection methodologies
The number of new vulnerabilities continues to rise significantly each year. Simultaneously,
vulnerability databases have challenges in promptly sharing new security events with …
vulnerability databases have challenges in promptly sharing new security events with …
Revisiting the performance of deep learning-based vulnerability detection on realistic datasets
The impact of software vulnerabilities on everyday software systems is concerning. Although
deep learning-based models have been proposed for vulnerability detection, their reliability …
deep learning-based models have been proposed for vulnerability detection, their reliability …
Hidden code vulnerability detection: A study of the Graph-BiLSTM algorithm
K Ge, QB Han - Information and Software Technology, 2024 - Elsevier
Context: The accelerated growth of the Internet and the advent of artificial intelligence have
led to a heightened interdependence of open source products, which has in turn resulted in …
led to a heightened interdependence of open source products, which has in turn resulted in …
Snopy: Bridging Sample Denoising with Causal Graph Learning for Effective Vulnerability Detection
Deep Learning (DL) has emerged as a promising means for vulnerability detection due to its
ability to automatically derive features from vulnerable code. Unfortunately, current solutions …
ability to automatically derive features from vulnerable code. Unfortunately, current solutions …
[HTML][HTML] Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities
This paper provides a comprehensive review of the future of cybersecurity through
Generative AI and Large Language Models (LLMs). We explore LLM applications across …
Generative AI and Large Language Models (LLMs). We explore LLM applications across …