12 citations · 13 across the 9 of their papers we have counts for
12 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…
Have You Ever Seen Them? Entity-level Membership Inference through Interrogating Large Language Models
Yiran Zhu, Ziqi Yang
Large Language Models (LLMs) raise growing concerns about privacy leakage and copyright compliance. Membership inference is a key tool for assessing such risks, but existing studie…
PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality
Zeyuan Chen, Ziqing Yang, Yihan Ma +2
As academic submissions grow, the traditional peer review process struggles to keep up, raising concerns about quality and fairness. A trend of using large language models (LLMs) f…
BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning
Ziqing Yang, Rui Wen, Xinlei He +3
Prompt learning is a new machine learning paradigm that has attracted ample attention due to its simplicity and proven efficacy. Despite its growing adoption, the security vulnerab…
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…
Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
Yiting Qu, Ziqing Yang, Yihan Ma +3
Recent advances in text-to-image diffusion models have enabled the creation of a new form of digital art: optical illusions--visual tricks that create different perceptions of real…
The Challenge of Identifying the Origin of Black-Box Large Language Models
Ziqing Yang, Yixin Wu, Yun Shen +3
The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the or…
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
Yixin Wu, Ziqing Yang, Yun Shen +2
Large language models (LLMs) have facilitated the generation of high-quality, cost-effective synthetic data for developing downstream models and conducting statistical analyses in…
Peering Behind the Shield: Guardrail Identification in Large Language Models
Ziqing Yang, Yixin Wu, Rui Wen +2
With the rapid adoption of large language models (LLMs), conversational AI agents have become widely deployed across real-world applications. To enhance safety, these agents are of…
SOS! Soft Prompt Attack Against Open-Source Large Language Models
Ziqing Yang, Michael Backes, Yang Zhang +1
Open-source large language models (LLMs) have become increasingly popular among both the general public and industry, as they can be customized, fine-tuned, and freely used. Howeve…
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…
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
Boyang Zhang, Zheng Li, Ziqing Yang +4
While advanced machine learning (ML) models are deployed in numerous real-world applications, previous works demonstrate these models have security and privacy vulnerabilities. Var…