8 papers · 1 filter
RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems
Niveen O. Jaffal, Ahmet Yuksel, David Mohaisen
Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a benchmark for RAG-style prompt-in…
Enhancing Reliability in LLM-Based Secure Code Generation
Mohammed F. Kharma, Mohammad Alkhanafseh, Ahmed Sabbah +1
Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting strategies. Existing prompt eng…
An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
Mohammed Kharma, Ahmed Sabbah, Mohammad Alkhanafseh +2
The growing use of Large Language Models (LLMs) for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code freque…
Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh +3
Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive mai…
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
Ahmed Sabbah, Mohammed Kharma, Radi Jarrar +2
We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extra…
A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
Mohammed Kharma, Ahmed Sabbah, Radi Jarrar +3
This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with improved security quality in LLM-as…
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
Niveen O. Jaffal, Mohammed Alkhanafseh, David Mohaisen
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident…
Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows
Jie Lin, David Mohaisen
This study examines the impact of tokenized Java code length on the accuracy and explicitness of ten major LLMs in vulnerability detection. Using chi-square tests and known ground…