17 citations · 32 across the 13 of their papers we have counts for
13 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…
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…
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…
HE-DAP: Homomorphic Encryption-based Dynamic Adaptive Parameter Optimization for Statistical Computation
Yun-Soo Park, Hyunmin Choi, Hyoungshick Kim +1
Homomorphic encryption (HE) enables privacy-preserving analytics but remains hindered by high computational overhead. We find that the inverse square root-a key primitive in many s…
Original Sin of npm: A Study on Vulnerability Propagation in JavaScript Dependency Networks
Michael Robinson, Sajal Halder, Muhammad Ejaz Ahmed +3
Understanding vulnerability propagation is essential for assessing how vulnerabilities spread across components of a software package. This supports more accurate impact analysis a…
Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
Bushra Sabir, Shigang Liu, Seung Ick Jang +6
Automatically generating source code from natural language using large language models (LLMs) is becoming common, yet security vulnerabilities persist despite advances in fine tuni…
When the Abyss Looks Back: Unveiling Evolving Dark Patterns in Cookie Consent Banners
Nivedita Singh, Seyoung Jin, Hyoungshick Kim
To comply with data protection regulations such as the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), websites widely deploy cookie co…
UniHENN: Designing Faster and More Versatile Homomorphic Encryption-based CNNs without im2col
Hyunmin Choi, Jihun Kim, Seungho Kim +4
Homomorphic encryption (HE) enables privacy-preserving deep learning by allowing computations on encrypted data without decryption. However, deploying convolutional neural networks…
Securing Cloud-Based Internet of Things: Challenges and Mitigations
Nivedita Singh, Rajkumar Buyya, Hyoungshich Kim
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have intro…
Crumbled Cookie Exploring E-commerce Websites Cookie Policies with Data Protection Regulations
Nivedita Singh, Yejin Do, Yongsang Yu. Imane Fouad +2
Despite stringent data protection regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other country-specific regulati…
Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models
Sanghak Oh, Kiho Lee, Seonhye Park +2
AI-powered coding assistant tools have revolutionized the software engineering ecosystem. However, prior work has demonstrated that these tools are vulnerable to poisoning attacks.…
Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication
Hyunmin Choi, Simon Woo, Hyoungshick Kim
Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns…
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
Seonhye Park, Alsharif Abuadbba, Shuo Wang +4
Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking…