15 citations · 21 across the 13 of their papers we have counts for
15 papers
Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other agents. Existing threat classific…
Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study
Zahra Mousavi, Chadni Islam, M. Ali Babar +2
AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs. These A…
DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense
Zhen Wang, Kristen Moore, Qin Wang +7
Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to h…
GRIDEX: Grid-Grounded Forensic Explanations for Deepfake Spectrogram Analysis
Thi Ngan Ha Do, Tingmin Wu, Alsharif Abuadbba +1
The advancement of speech generation technologies has made artificial speech increasingly realistic. Although modern classification models can achieve high accuracy when it comes t…
APT-Agent: Automated Penetration Testing using Large Language Models
William Guanting Li, Alsharif Abuadbba, Kristen Moore +1
Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and complexity. Large Language Models…
Can Current Detectors Catch Face-to-Voice Deepfake Attacks?
Nguyen Linh Bao Nguyen, Alsharif Abuadbba, Kristen Moore +1
The rapid advancement of generative models has enabled the creation of increasingly stealthy synthetic voices, commonly referred to as audio deepfakes. A recent technique, FOICE [U…
Adversarial Attacks Against Automated Fact-Checking: A Survey
Fanzhen Liu, Alsharif Abuadbba, Kristen Moore +5
In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) h…
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Fanzhen Liu, Xiaoxiao Ma, Jian Yang +6
Enhancing the interpretability of graph neural networks (GNNs) is crucial to ensure their safe and fair deployment. Recent work has introduced self-explainable GNNs that generate e…
From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore +4
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simula…
LLMs Are Not Yet Ready for Deepfake Image Detection
Shahroz Tariq, David Nguyen, M. A. P. Chamikara +3
The growing sophistication of deepfakes presents substantial challenges to the integrity of media and the preservation of public trust. Concurrently, vision-language models (VLMs),…
From Solitary Directives to Interactive Encouragement! LLM Secure Code Generation by Natural Language Prompting
Shigang Liu, Bushra Sabir, Seung Ick Jang +5
Large Language Models (LLMs) have shown remarkable potential in code generation, making them increasingly important in the field. However, the security issues of generated code hav…
An Investigation into Misuse of Java Security APIs by Large Language Models
Zahra Mousavi, Chadni Islam, Kristen Moore +2
The increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are expl…
SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
Binh M. Le, Jiwon Kim, Simon S. Woo +3
Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. How…
Towards Understanding of Deepfake Videos in the Wild
Beomsang Cho, Binh M. Le, Jiwon Kim +4
Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing dataset…
RAIDER: Reinforcement-aided Spear Phishing Detector
Keelan Evans, Alsharif Abuadbba, Tingmin Wu +5
Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniqu…