17 citations · 44 across the 14 of their papers we have counts for
15 papers
Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
Xuan Chen, Shiwei Feng, Zikang Xiong +6
Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing wo…
Position: Intelligent Coding Systems Should Write Programs with Justifications
Xiangzhe Xu, Shiwei Feng, Zian Su +2
Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven co…
TAI3: Testing Agent Integrity in Interpreting User Intent
Shiwei Feng, Xiangzhe Xu, Xuan Chen +5
LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation o…
RFCAudit: An LLM Agent for Functional Bug Detection in Network Protocols
Mingwei Zheng, Chengpeng Wang, Xuwei Liu +3
Functional correctness is critical for ensuring the reliability and security of network protocol implementations. Functional bugs, instances where implementations diverge from beha…
ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation
Shiwei Feng, Yapeng Ye, Qingkai Shi +5
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reli…
EffiTune: Diagnosing and Mitigating Training Inefficiency for Parameter Tuner in Robot Navigation System
Shiwei Feng, Xuan Chen, Zikang Xiong +5
Robot navigation systems are critical for various real-world applications such as delivery services, hospital logistics, and warehouse management. Although classical navigation met…
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo +4
Pixel-wise regression tasks (e.g., monocular depth estimation (MDE) and optical flow estimation (OFE)) have been widely involved in our daily life in applications like autonomous d…
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
Siyuan Cheng, Guanhong Tao, Yingqi Liu +7
Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This suscept…
PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model
Xiangzhe Xu, Zhou Xuan, Shiwei Feng +7
Binary similarity analysis determines if two binary executables are from the same source program. Existing techniques leverage static and dynamic program features and may utilize a…
Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Xiangzhe Xu, Zhuo Zhang, Zian Su +8
Decompilation aims to recover the source code form of a binary executable. It has many security applications, such as malware analysis, vulnerability detection, and code hardening.…
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
Zhiyuan Cheng, Hongjun Choi, James Liang +5
Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception, particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is t…
Detecting Backdoors in Pre-trained Encoders
Shiwei Feng, Guanhong Tao, Siyuan Cheng +6
Self-supervised learning in computer vision trains on unlabeled data, such as images or (image, text) pairs, to obtain an image encoder that learns high-quality embeddings for inpu…
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense
Siyuan Cheng, Guanhong Tao, Yingqi Liu +8
Deep Learning backdoor attacks have a threat model similar to traditional cyber attacks. Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of imp…
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu +8
Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario…
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
Shucheng Li, Lingfei Wu, Shiwei Feng +3
The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem s…