1.3k citations · 2.1k across the 28 of their papers we have counts for
10 papers · 1 filter
Natural Code Transformation Confuses Agents in Vulnerability Analysis
Jie Zhu, Ronik Bhaskar, Heather Zheng +2
Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today's software systems need mechani…
Symbolon: Symbolic Execution by Learning Code Transformation
Jie Zhu, Penghui Li, Zhongxuan Li +4
Symbolic execution is a powerful program analysis technique with broad applications, such as vulnerability detection, security testing, and malware analysis. However, this techniqu…
SoK: AI-Augmented Binary Reversing
Yujeong Kwon, Yiyue Zhang, Kexin Pei +2
Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains inherently challenging due to…
Locus: Agentic Predicate Synthesis for Directed Fuzzing
Jie Zhu, Chihao Shen, Ziyang Li +3
Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, an…
Comment on Revisiting Neural Program Smoothing for Fuzzing
Dongdong She, Kexin Pei, Junfeng Yang +2
MLFuzz, a work accepted at ACM FSE 2023, revisits the performance of a machine learning-based fuzzer, NEUZZ. We demonstrate that its main conclusion is entirely wrong due to severa…
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…
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…
NEUZZ: Efficient Fuzzing with Neural Program Smoothing
Dongdong She, Kexin Pei, Dave Epstein +3
Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger so…
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Kexin Pei, Linjie Zhu, Yinzhi Cao +3
Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavio…