37 citations · 148 across the 17 of their papers we have counts for
17 papers
ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search
Zeyu Shen, Basileal Imana, Tong Wu +3
Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain vulnerable to attacks on the re…
Does More Inference-Time Compute Really Help Robustness?
Tong Wu, Chong Xiang, Jiachen T. Wang +4
Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we first show that smaller-…
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Dennis Jacob, Chong Xiang, Prateek Mittal
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophical…
Effectively Controlling Reasoning Models through Thinking Intervention
Tong Wu, Chong Xiang, Jiachen T. Wang +2
Reasoning-enhanced large language models (LLMs) explicitly generate intermediate reasoning steps prior to generating final answers, helping the model excel in complex problem-solvi…
Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy
Tong Wu, Shujian Zhang, Kaiqiang Song +7
Large Language Models (LLMs) are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests. One major cause of these vulnerabili…
Certifiably Robust RAG against Retrieval Corruption
Chong Xiang, Tong Wu, Zexuan Zhong +3
Retrieval-augmented generation (RAG) is susceptible to retrieval corruption attacks, where malicious passages injected into retrieval results can lead to inaccurate model responses…
Position: Towards Resilience Against Adversarial Examples
Sihui Dai, Chong Xiang, Tong Wu +1
Current research on defending against adversarial examples focuses primarily on achieving robustness against a single attack type such as or -bounded attack…
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
Chong Xiang, Tong Wu, Sihui Dai +3
State-of-the-art defenses against adversarial patch attacks can now achieve strong certifiable robustness with a marginal drop in model utility. However, this impressive performanc…
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Sihui Dai, Saeed Mahloujifar, Chong Xiang +3
The bulk of existing research in defending against adversarial examples focuses on defending against a single (typically bounded Lp-norm) attack, but for a practical setting, machi…
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking
Chong Xiang, Alexander Valtchanov, Saeed Mahloujifar +1
Object detectors, which are widely deployed in security-critical systems such as autonomous vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a sing…
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Chong Xiang, Saeed Mahloujifar, Prateek Mittal
The adversarial patch attack against image classification models aims to inject adversarially crafted pixels within a restricted image region (i.e., a patch) for inducing model mis…
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches
Chong Xiang, Prateek Mittal
An adversarial patch can arbitrarily manipulate image pixels within a restricted region to induce model misclassification. The threat of this localized attack has gained significan…
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina +4
While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-w…
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
Chong Xiang, Prateek Mittal
State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an adversary introduces a small adversarial patch to make detectors miss the detection of…
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking
Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag +1
Localized adversarial patches aim to induce misclassification in machine learning models by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be…
Differentially Private Data Generative Models
Qingrong Chen, Chong Xiang, Minhui Xue +4
Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation re…
Generating 3D Adversarial Point Clouds
Chong Xiang, Charles R. Qi, Bo Li
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examp…