6 citations · 10 across the 11 of their papers we have counts for
13 papers
Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-to-Text Jailbreaks
Boyuan Chen, Yehia Dawoud, Hailemariam Mersha +4
Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four published attack families on a 313…
Where Does the Audio Jailbreak Live? A Controlled Frequency-Depth Audit of AdvWave-P on Qwen2-Audio
Boyuan Chen, Minseok Kim, Sohaila Abdulsattar +4
We audit frequency and decoder-depth claims for AdvWave-P, an additive audio jailbreak, on Qwen2-Audio. The protocol masks frequency components of the perturbation in the short-tim…
HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization
Jianzhi Shen, Keyu Mao, Minghao Shao +7
Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction. Existing training-free metho…
VHDLSuite: Unified Pipeline for LLM VHDL Generation with Data Synthesis and Evaluation
Yijun Shen, Minghao Shao, Yichen Zhao +4
Large Language Models (LLM) have shown impressive capabilities in Register Transfer Level (RTL) code generation, particularly for Verilog. However, evaluating their performance wit…
Code for All: Educational Applications of the "Vibe Coding" Hackathon in Programming Education across All Skill Levels
Ashley J. Chen, Yijia Cao, Minghao Shao +2
The emergence of large language models has enabled vibe coding, a natural language approach to programming in which users describe intent and AI generates or revises code, potentia…
GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations
Boyuan Chen, Minghao Shao, Siddharth Garg +2
Vision Language Models (VLMs) exhibit persistent hallucinations in counting tasks, with accuracy substantially lower than other visual reasoning tasks (excluding sentiment). This p…
From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
Haoran Xi, Minghao Shao, Brendan Dolan-Gavitt +2
Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file…
MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
Boyuan Chen, Minghao Shao, Abdul Basit +2
As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and s…
CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
Minghao Shao, Haoran Xi, Nanda Rani +9
Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated…
D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
Meet Udeshi, Minghao Shao, Haoran Xi +9
Large Language Models (LLMs) have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to a…
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Minghao Shao, Sofija Jancheska, Meet Udeshi +10
Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoro…
Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing
Boyuan Chen, Mingzhi Zhu, Brendan Dolan-Gavitt +2
The rapid advancement of large language models (LLMs) has significantly improved code completion tasks, yet the trade-off between accuracy and computational cost remains a critical…
An Empirical Evaluation of LLMs for Solving Offensive Security Challenges
Minghao Shao, Boyuan Chen, Sofija Jancheska +4
Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs…