12 citations · 13 across the 11 of their papers we have counts for
11 papers
EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models
Yiting Qu, Ziqing Yang, Chi Cui +3
Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet whether these hidden CoTs can be dire…
GEO-Flag: Detecting and Measuring GEO-Optimized Web Content
Junjie Chu, Ye Leng, Mingjie Li +3
Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized…
Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation
Ye Leng, Junjie Chu, Yiting Qu +3
Picture books and comics have long been used to disseminate hateful narratives because they are easily understood even by children, as exemplified by the notorious Nazi propaganda…
When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm
Ye Leng, Junjie Chu, Mingjie Li +5
Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much strong…
Understanding LLM Behavior When Encountering User-Supplied Harmful Content in Harmless Tasks
Junjie Chu, Yiting Qu, Ye Leng +4
Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute directly harmful tasks. However, a sub…
Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks
Junjie Chu, Xinyue Shen, Ye Leng +3
The rapid expansion of research in LLM safety presents challenges in tracking advancements, making benchmarks important evaluation infrastructures for identifying key trends and fa…
JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring
Junjie Chu, Mingjie Li, Ziqing Yang +6
Accurately determining whether a jailbreak attempt has succeeded is a fundamental yet unresolved challenge. Existing evaluation methods rely on misaligned proxy indicators or naive…
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li +4
Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However…
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities
Junjie Chu, Prashant Singh, Salman Toor
In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many e…
JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
Junjie Chu, Yugeng Liu, Ziqing Yang +3
Jailbreak attacks aim to bypass the LLMs' safeguards. While researchers have proposed different jailbreak attacks in depth, they have done so in isolation -- either with unaligned…
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models
Junjie Chu, Zeyang Sha, Michael Backes +1
Significant advancements have recently been made in large language models represented by GPT models. Users frequently have multi-round private conversations with cloud-hosted GPT m…