44 citations · 256 across the 65 of their papers we have counts for
69 papers
Continuous Assurance of Agentic Security Auditors for Software Delivery Decision Gates
Guy Lupo, Nguyen Hung Nguyen, Viet Vo +4
Large language model (LLM)-based repository auditors are increasingly deployed as security controls within continuous integration (CI) pipelines, where their findings admit, block,…
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…
DEFEAT: Stitching Fragmented File I/O Contexts for Early Ransomware Detection
Muhammad Ejaz Ahmed, Hyoungshick Kim, Mohsen Ali Alawami +4
Ransomware increasingly fragments its file operations across temporary and intermediate files, scattering the semantic context that links individual I/O events to an overarching en…
Shadow Queries for Private Retrieval in Vector Databases
Xinguo Feng, Zhongkui Ma, Zihan Wang +4
Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmented Generation (RAG), to incorporate domain-specific knowledge without…
Shifting from Injection to Interaction: Rethinking Web Security in the Age of LLMs and Beyond
Nivedita Singh, Alsharif Abuadbba, Yansong Gao +2
Large language models (LLMs) are becoming integral to web applications and browser agents, transforming online interactions while introducing new attack vectors and reshaping longs…
DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination
Fajar Wijitrisnanto, Alsharif Abuadbba, Yansong Gao +1
The timely dissemination of cyber threat intelligence (CTI) is critical for organizations to mount swift and effective incident response. When valid CTI is delivered to the right s…
GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting
Omri Ram, Mitchell Horner, Ron Van der Meyden +2
Vulnerability datasets underpin a wide range of security research, including vulnerability detection, automated remediation, and secure code generation. However, existing datasets…
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…
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…
Five Queries Are Enough: Query-Efficient and Surrogate-Free Membership Inference Attacks on RAG via Entailment
Nguyen Linh Bao Nguyen, Wanlun Ma, Viet Vo +4
Retrieval-augmented generation (RAG) has become central to large language model (LLM) deployments, grounding responses in enterprise or proprietary data to reduce hallucinations. H…
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
Xinguo Feng, Zhongkui Ma, Zihan Wang +2
Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs),…
Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework
Alsharif Abuadbba, Nazatul Sultan, Surya Nepal +1
AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open, cross-organizational environments. As a…
NADD: Amplifying Noise for Effective Diffusion-based Adversarial Purification
David D. Nguyen, The-Anh Ta, Yansong Gao +1
The strategy of combining diffusion-based generative models with classifiers continues to demonstrate state-of-the-art performance on adversarial robustness benchmarks. Known as ad…
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…
RINSER: Accurate API Prediction Using Masked Language Models
Muhammad Ejaz Ahmed, Christopher Cody, Muhammad Ikram +5
Malware authors commonly use obfuscation to hide API identities in binary files, making analysis difficult and time-consuming for a human expert to understand the behavior and inte…
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…
Uncovering Gradient Inversion Risks in Practical Language Model Training
Xinguo Feng, Zhongkui Ma, Zihan Wang +4
The gradient inversion attack has been demonstrated as a significant privacy threat to federated learning (FL), particularly in continuous domains such as vision models. In contras…
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),…
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen +3
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable…
Enhancing Malware Fingerprinting through Analysis of Evasive Techniques
Alsharif Abuadbba, Sean Lamont, Ejaz Ahmed +6
As malware detection evolves, attackers adopt sophisticated evasion tactics. Traditional file-level fingerprinting, such as cryptographic and fuzzy hashes, is often overlooked as a…
EPhishCADE: A Privacy-Aware Multi-Dimensional Framework for Email Phishing Campaign Detection
Wei Kang, Nan Wang, Jang Seung +2
Phishing attacks, typically carried out by email, remain a significant cybersecurity threat with attackers creating legitimate-looking websites to deceive recipients into revealing…