14 citations · 43 across the 11 of their papers we have counts for
12 papers
Recovering Process Variables from Industrial Network Traffic via Search-Based Optimization
Chuan Sheng, Shan Jiang, Jianming Zhao +1
Process variables (PVs) provide the process evidence needed for process-aware security monitoring in industrial cyber-physical systems (CPSs). However, existing supervisory infrast…
DeltaMCP: Incremental Regeneration via Spec-Aware Transformation for MCP servers
Aditya Pujara, Xiaogang Zhu, Hsiang-Ting Chen
The rapid development of LLMs coupled with the introduction of Model Context Protocol (MCP) has revolutionized how intelligent agents interact with APIs through deterministic and s…
From Storage to Steering: Memory Control Flow Attacks on LLM Agents
Zhenlin Xu, Xiaogang Zhu, Yu Yao +2
Modern agentic systems allow Large Language Model (LLM) agents to tackle complex tasks through extensive tool usage, forming structured control flows of tool selection and executio…
MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security
Yongjian Guo, Puzhuo Liu, Wanlun Ma +5
The Model Context Protocol (MCP) has emerged as a universal standard that enables AI agents to seamlessly connect with external tools, significantly enhancing their functionality.…
Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models
Kunpeng Zhang, Shuai Wang, Jitao Han +4
Deep learning (DL) libraries, widely used in AI applications, often contain vulnerabilities like buffer overflows and use-after-free errors. Traditional fuzzing struggles with the…
SITransformer: Shared Information-Guided Transformer for Extreme Multimodal Summarization
Sicheng Liu, Lintao Wang, Xiaogang Zhu +3
Extreme Multimodal Summarization with Multimodal Output (XMSMO) becomes an attractive summarization approach by integrating various types of information to create extremely concise…
SHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection
Kunpeng Zhang, Xiaogang Zhu, Xi Xiao +3
Mutation-based fuzzing is popular and effective in discovering unseen code and exposing bugs. However, only a few studies have concentrated on quantifying the importance of input b…
Path Transitions Tell More:Optimizing Fuzzing Schedules via Runtime Program States
Kunpeng Zhang, Xi Xiao, Xiaogang Zhu +3
Coverage-guided Greybox Fuzzing (CGF) is one of the most successful and widely-used techniques for bug hunting. Two major approaches are adopted to optimize CGF: (i) to reduce sear…
Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference
Xiaotao Feng, Ruoxi Sun, Xiaogang Zhu +5
The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and…
DeFuzz: Deep Learning Guided Directed Fuzzing
Xiaogang Zhu, Shigang Liu, Xian Li +4
Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedne…
A Feature-Oriented Corpus for Understanding, Evaluating and Improving Fuzz Testing
Xiaogang Zhu, Xiaotao Feng, Tengyun Jiao +4
Fuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs…
Bug Searching in Smart Contract
Xiaotao Feng, Qin Wang, Xiaogang Zhu +1
With the frantic development of smart contracts on the Ethereum platform, its market value has also climbed. In 2016, people were shocked by the loss of nearly $50 million in crypt…