21 citations · 42 across the 14 of their papers we have counts for
10 papers · 1 filter
Beyond Context: Large Language Models' Failure to Grasp Users' Intent
Ahmed M. Hussain, Salahuddin Salahuddin
Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability to understand context and recog…
Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection
Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li +1
Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everyth…
Jailbreaking Large Language Models Through Content Concretization
Johan Wahréus, Ahmed Hussain, Panos Papadimitratos
Large Language Models (LLMs) are increasingly deployed for task automation and content generation, yet their safety mechanisms remain vulnerable to circumvention through different…
NEFMind: Parameter-Efficient Fine-Tuning of Open-Source LLMs for Telecom APIs Automation
Zainab Khan, Ahmed Hussain, Mukesh Thakur +2
The use of Service-Based Architecture in modern telecommunications has exponentially increased Network Functions (NFs) and Application Programming Interfaces (APIs), creating subst…
Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens
Salahuddin Salahuddin, Ahmed Hussain, Jussi Löppönen +1
The increasing scale of AI workloads demands High-Performance Computing (HPC) infrastructure and training methodologies that are both scalable and sustainable. While Large Language…
AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons
Hexu Li, Konstantinos Kalogiannis, Ahmed Mohamed Hussain +1
Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything (V2X) communications, offers significant benefits in fuel efficiency and ro…
Privacy-Preserving Secure Neighbor Discovery for Wireless Networks
Ahmed Mohamed Hussain, Panos Papadimitratos
Traditional Neighbor Discovery (ND) and Secure Neighbor Discovery (SND) are key elements for network functionality. SND is a hard problem, satisfying not only typical security prop…
Prompt, Divide, and Conquer: Bypassing Large Language Model Safety Filters via Segmented and Distributed Prompt Processing
Johan Wahréus, Ahmed Hussain, Panos Papadimitratos
Large Language Models (LLMs) have transformed task automation and content generation across various domains while incorporating safety filters to prevent misuse. We introduce a nov…
LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis
Saeif Alhazbi, Ahmed Mohamed Hussain, Gabriele Oligeri +1
As Large Language Models (LLMs) become increasingly integrated into many technological ecosystems across various domains and industries, identifying which model is deployed or bein…
CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models
Johan Wahréus, Ahmed Mohamed Hussain, Panos Papadimitratos
Numerous studies have investigated methods for jailbreaking Large Language Models (LLMs) to generate harmful content. Typically, these methods are evaluated using datasets of malic…