74 citations · 184 across the 12 of their papers we have counts for
12 papers
Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning
Xiaotian Zhou, Di Tang, Xiaofeng Wang +1
Large Language Models (LLMs) have shown a high capability in answering questions on a diverse range of topics. However, these models sometimes produce biased, ideologized or incorr…
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
IndirectAD: Practical Data Poisoning Attacks against Recommender Systems for Item Promotion
Zihao Wang, Tianhao Mao, XiaoFeng Wang +2
Recommender systems play a central role in digital platforms by providing personalized content. They often use methods such as collaborative filtering and machine learning to accur…
LLM-Enhanced Software Patch Localization
Jinhong Yu, Yi Chen, Di Tang +4
Open source software (OSS) is integral to modern product development, and any vulnerability within it potentially compromises numerous products. While developers strive to apply se…
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering
Rui Zhu, Di Tang, Siyuan Tang +4
Most existing methods to detect backdoored machine learning (ML) models take one of the two approaches: trigger inversion (aka. reverse engineer) and weight analysis (aka. model di…
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models
Rui Zhu, Di Tang, Siyuan Tang +2
In this paper, we present a simple yet surprisingly effective technique to induce "selective amnesia" on a backdoored model. Our approach, called SEAM, has been inspired by the pro…
Understanding Impacts of Task Similarity on Backdoor Attack and Detection
Di Tang, Rui Zhu, XiaoFeng Wang +2
With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defend…
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
Jiawei Liu, Yangyang Kang, Di Tang +5
Neural text ranking models have witnessed significant advancement and are increasingly being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of g…
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
Shuaike Dong, Zhou Li, Di Tang +3
The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing t…
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection
Di Tang, XiaoFeng Wang, Haixu Tang +1
A security threat to deep neural networks (DNN) is backdoor contamination, in which an adversary poisons the training data of a target model to inject a Trojan so that images carry…
Invisible Mask: Practical Attacks on Face Recognition with Infrared
Zhe Zhou, Di Tang, Xiaofeng Wang +3
Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross…
Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints
Di Tang, XiaoFeng Wang, Kehuan Zhang
To launch black-box attacks against a Deep Neural Network (DNN) based Face Recognition (FR) system, one needs to build \textit{substitute} models to simulate the target model, so t…