12 citations · 35 across the 42 of their papers we have counts for
13 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…
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…
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…
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…