Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

Abstract While Large Language Models (LLMs) display versatile functionality, they continue
to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human …

Large Language Models (LLMs) are increasingly being integrated into applications, with
versatile functionalities that can be easily modulated via natural language prompts. So far, it …

Large Language Models (LLMs), renowned for their superior proficiency in language
comprehension and generation, stimulate a vibrant ecosystem of applications around them …

Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) services due
to their exceptional proficiency in understanding and generating human-like text. LLM …

Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …

Abstract Large Language Models (LLMs) have the capacity to store and recall facts. Through
experimentation with open-source models, we observe that this ability to retrieve facts can …

Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …