9 citations · 10 across the 9 of their papers we have counts for
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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…
Poster: Mind the Gap -- Characterizing the Temporal Blind Spot Between GSB and DNS Resolution
Tomer Gal, Fujiao Ji, Doowon Kim +1
Google Safe Browsing (GSB) and DNS resolution operate concurrently during browser navigation, yet their packet-level synchronization remains understudied. This work characterizes t…
What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
Qin Yang, Lu Malloy, Joshua Lee +4
Large language model (LLM)-powered content moderation systems are a critical defense against harmful online content. However, they operate primarily on tokenized text and often ove…
How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models
Fujiao Ji, Doowon Kim
Large language models (LLMs) have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor gener…
A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs
Kiho Lee, Jungkon Kim, Doowon Kim +1
Code-generating Large Language Models (LLMs) significantly accelerate software development. However, their frequent generation of insecure code presents serious risks. We present a…
Registration, Detection, and Deregistration: Analyzing DNS Abuse for Phishing Attacks
Kyungchan Lim, Raffaele Sommese, Mattis Jonker +3
Phishing continues to pose a significant cybersecurity threat. While blocklists currently serve as a primary defense, due to their reactive, passive nature, these delayed responses…
An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection
Shenao Yan, Shen Wang, Yue Duan +4
Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fin…
Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models
Fujiao Ji, Kiho Lee, Hyungjoon Koo +4
Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual si…
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.…