9 citations · 10 across the 5 of their papers we have counts for
5 papers
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.…