8 citations · 9 across the 4 of their papers we have counts for
5 papers
PhishGen: Understanding LLM Capabilities in Automated Phishing Website Generation
Fujiao Ji, Yerim Kim, Yena Cho +3
Phishing relies on visual deception, traditionally requiring web-development skills or pre-built kits. LLMs erode this barrier by generating functional websites from natural-langua…
Aware but Unprepared: Measuring the Security Awareness-Behavior Gap in Student Use of LLM-Generated Code with Bifröst
Jaehwan Park, Seonhye Park, Kyungchan Lim +2
The advent of Artificial Intelligence (AI), particularly large language models (LLMs), has revolutionized software development by enabling developers to specify tasks in natural la…
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…
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.…
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…