1.3k citations · 2.7k across the 38 of their papers we have counts for
39 papers
TrustLLM: Trustworthiness in Large Language Models
Yue Huang, Lichao Sun, Haoran Wang +67
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…
Exploiting Code Symmetries for Learning Program Semantics
Kexin Pei, Weichen Li, Qirui Jin +5
This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We in…
NeuDep: Neural Binary Memory Dependence Analysis
Kexin Pei, Dongdong She, Michael Wang +6
Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias informa…
: Rigorous and Efficient Directed Greybox Fuzzing
Abhishek Shah, Dongdong She, Samanway Sadhu +3
Directed greybox fuzzing is a popular technique for targeted software testing that seeks to find inputs that reach a set of target sites in a program. Most existing directed greybo…
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Huan Zhang, Shiqi Wang, Kaidi Xu +5
Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robu…
Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
Dongdong She, Abhishek Shah, Suman Jana
Seed scheduling, the order in which seeds are selected, can greatly affect the performance of a fuzzer. Existing approaches schedule seeds based on their historical mutation data,…
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
Suyoung Lee, Wonho Song, Suman Jana +2
Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners. In this paper, we argue that…
Learning Security Classifiers with Verified Global Robustness Properties
Yizheng Chen, Shiqi Wang, Yue Qin +3
Many recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all…
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
Shiqi Wang, Huan Zhang, Kaidi Xu +4
Bound propagation based incomplete neural network verifiers such as CROWN are very efficient and can significantly accelerate branch-and-bound (BaB) based complete verification of…
Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity
Kexin Pei, Zhou Xuan, Junfeng Yang +2
Detecting semantically similar functions -- a crucial analysis capability with broad real-world security usages including vulnerability detection, malware lineage, and forensics --…
XDA: Accurate, Robust Disassembly with Transfer Learning
Kexin Pei, Jonas Guan, David Williams-King +2
Accurate and robust disassembly of stripped binaries is challenging. The root of the difficulty is that high-level structures, such as instruction and function boundaries, are abse…
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Kaidi Xu, Huan Zhang, Shiqi Wang +4
Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
Towards Understanding Fast Adversarial Training
Bai Li, Shiqi Wang, Suman Jana +1
Current neural-network-based classifiers are susceptible to adversarial examples. The most empirically successful approach to defending against such adversarial examples is adversa…
MTFuzz: Fuzzing with a Multi-Task Neural Network
Dongdong She, Rahul Krishna, Lu Yan +2
Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage.…
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li, Shiqi Wang, Yunhan Jia +4
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the orig…
Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks (Extended Version)
Jianan Yao, Gabriel Ryan, Justin Wong +2
Verifying real-world programs often requires inferring loop invariants with nonlinear constraints. This is especially true in programs that perform many numerical operations, such…
HYDRA: Pruning Adversarially Robust Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural ne…
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Somali Chaterji, Parinaz Naghizadeh, Muhammad Ashraful Alam +14
Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-…
Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures
Saurabh Bagchi, Vaneet Aggarwal, Somali Chaterji +13
A set of about 80 researchers, practitioners, and federal agency program managers participated in the NSF-sponsored Grand Challenges in Resilience Workshop held on Purdue campus on…
Cost-Aware Robust Tree Ensembles for Security Applications
Yizheng Chen, Shiqi Wang, Weifan Jiang +2
There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be…
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
Gabriel Ryan, Justin Wong, Jianan Yao +2
Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the…
Fine Grained Dataflow Tracking with Proximal Gradients
Gabriel Ryan, Abhishek Shah, Dongdong She +2
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel…
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Abhishek Shah +2
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, wh…