12 citations · 46 across the 52 of their papers we have counts for
18 papers · 1 filter
GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
Xinyu Zhang, Yixin Wu, Boyang Zhang +4
Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Mode…
AttackPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents
Yixin Wu, Rui Wen, Chi Cui +2
Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack parameters for optimal estimation are…
From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection
Mengfei Liang, Yiting Qu, Yukun Jiang +2
The rapid evolution of AI-generated images poses growing challenges to information integrity and media authenticity. Existing detection approaches face limitations in robustness, i…
Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency
Yukun Jiang, Mingjie Li, Michael Backes +1
Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful content, a risk that has been further…
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…
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
Yiting Qu, Michael Backes, Yang Zhang
Vision-language models (VLMs) are increasingly applied to identify unsafe or inappropriate images due to their internal ethical standards and powerful reasoning abilities. However,…
Amplifying Machine Learning Attacks Through Strategic Compositions
Yugeng Liu, Zheng Li, Hai Huang +2
Machine learning (ML) models are proving to be vulnerable to a variety of attacks that allow the adversary to learn sensitive information, cause mispredictions, and more. While the…
Excessive Reasoning Attack on Reasoning LLMs
Wai Man Si, Mingjie Li, Michael Backes +1
Recent reasoning large language models (LLMs), such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior st…
Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes +2
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and ques…
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark
Rui Wen, Yiyong Liu, Michael Backes +1
Data reconstruction attacks, which aim to recover the training dataset of a target model with limited access, have gained increasing attention in recent years. However, there is cu…
When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
Xinyue Shen, Yun Shen, Michael Backes +1
Knowledge files have been widely used in large language model (LLM) agents, such as GPTs, to improve response quality. However, concerns about the potential leakage of knowledge fi…
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…
Fairness and/or Privacy on Social Graphs
Bartlomiej Surma, Michael Backes, Yang Zhang
Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in…
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…
HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
Xinyue Shen, Yixin Wu, Yiting Qu +3
Large Language Models (LLMs) have raised increasing concerns about their misuse in generating hate speech. Among all the efforts to address this issue, hate speech detectors play a…
SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation
Mingjie Li, Wai Man Si, Michael Backes +2
As advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) methods (e.g., LoRA) will become e…