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Found 7 papers, 3 papers with code
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).
Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models
This study provides a comparative analysis of state-of-the-art large language models (LLMs), analyzing how likely they generate vulnerabilities when writing simple C programs using a neutral zero-shot prompt.
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
The results can serve as a reference for comparing the general cybersecurity knowledge of humans and LLMs.
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs?
SecureFalcon achieves 94% accuracy in binary classification and up to 92% in multiclassification, with instant CPU inference times.
The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification.
Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures.
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and extract counterexamples.
