activity
20192026
most citedSHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection

14 citations · 43 across the 11 of their papers we have counts for

collaborators
Showing cs.CRShow all

7 papers · 1 filter

cs.CR2026

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…

cs.CR2026

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…

cs.CR2025★ 1 cited

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.…

cs.CR2023★ 14 cited

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…

cs.CR2022★ 2 cited

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…

cs.CR2021★ 8 cited

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…

cs.CR2020★ 7 cited

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…