About

I am a Research Scientist in the AI Supply Chain Security Group at IBM Research. My research areas are software and systems security, program analysis, AI for security, autonomous vulnerability discovery, secure software supply chains, and the security of AI agents.

I build program-analysis and AI systems that discover, prioritize, and mitigate vulnerabilities in large-scale software, from OS kernels and open-source infrastructure to AI-generated code and multi-agent AI systems. Recent work in this line includes LLM-driven large-scale bug discovery (ICML 2026) and LLM-aided categorization of security patches (NDSS 2026).

I received my Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities (advisor: Kangjie Lu), and my B.Eng. in Information Security from the University of Science and Technology of China.

News

2026
“One Bug, Hundreds Behind: LLMs for Large-Scale Bug Discovery” appears at ICML 2026.
2026
“What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs” appears at NDSS 2026.
2025
APILOT is accepted at ACSAC 2025 and awarded ACSAC’s Code Reviewed badge.
2025
Serving on the NDSS 2025 program committee.
2024
Presented GNNIC at NDSS 2024.
2024
Co-organized the AISCC workshop, co-located with NDSS 2024, and served on the IEEE S&P 2024 program committee.
Dec 2023
U.S. Patent 11,853,751, “Indirect Function Call Target Identification in Software,” is granted.
Jul 2023
Joined IBM Research as a Research Scientist.

Publications

Peer-reviewed conference and journal papers, with workshop papers and preprints marked inline. * denotes co-first authors.

2026

  1. One Bug, Hundreds Behind: LLMs for Large-Scale Bug Discovery
    Qiushi Wu, Yue Xiao, Dhilung Kirat, Kevin Eykholt, Jiyong Jang, and Douglas Lee Schales
    ICML 2026 · International Conference on Machine Learning [paper] [arXiv] [dataset]
  2. What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs
    Xingyu Li, Juefei Pu, Yifan Wu, Xiaochen Zou, Shitong Zhu, Qiushi Wu, Zheng Zhang, Joshua Hsu, Yue Dong, Zhiyun Qian, Kangjie Lu, Trent Jaeger, Michael De Lucia, and Srikanth V. Krishnamurthy
    NDSS 2026 · Network and Distributed System Security Symposium [paper] [code]

2025

  1. APILOT: Improving the Security and Usability of LLM Code Suggestions via Outdated API Mitigation
    Weiheng Bai, Keyang Xuan, Pengxiang Huang, Qiushi Wu, Jianing Wen, Jingjing Wu, and Kangjie Lu
    ACSAC 2025 · Annual Computer Security Applications Conference · Code Reviewed badge [paper] [code]
  2. AFLGopher: Accelerating Directed Fuzzing via Feasibility-Aware Guidance
    Weiheng Bai, Kefu Wu, Qiushi Wu, and Kangjie Lu
    arXiv 2025 · Preprint [arXiv]

2024

  1. GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity
    Qiushi Wu, Zhongshu Gu, Hani Jamjoom, and Kangjie Lu
    NDSS 2024 · Network and Distributed System Security Symposium [paper]
  2. Exploring the Influence of Prompts in LLMs for Security-Related Tasks
    Weiheng Bai, Kefu Wu, Qiushi Wu, and Kangjie Lu
    NDSS Workshop 2024 · AISCC, co-located with NDSS · Workshop paper [paper]

2023

  1. Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs
    Jianhao Xu, Kangjie Lu, Zhengjie Du, Zhu Ding, Linke Li, Qiushi Wu, Mathias Payer, and Bing Mao
    USENIX Security 2023 · USENIX Security Symposium [paper]
  2. Guiding Directed Fuzzing with Feasibility
    Weiheng Bai, Kefu Wu, Qiushi Wu, and Kangjie Lu
    EuroS&P Workshops 2023 · IEEE European Symposium on Security and Privacy Workshops · Workshop paper [paper]
  3. Towards More Effective Responsible Disclosure for Vulnerability Research
    Weiheng Bai and Qiushi Wu
    EthiCS 2023 · Workshop on Ethics in Computer Security · Workshop paper [paper]

2022

  1. Non-Distinguishable Inconsistencies as a Deterministic Oracle for Detecting Security Bugs
    Qingyang Zhou, Qiushi Wu, Dinghao Liu, Shouling Ji, and Kangjie Lu
    CCS 2022 · ACM Conference on Computer and Communications Security [paper] [code]
  2. OS-Aware Vulnerability Prioritization via Differential Severity Analysis
    Qiushi Wu*, Yue Xiao*, Xiaojing Liao, and Kangjie Lu (*co-first authors)
    USENIX Security 2022 · USENIX Security Symposium [paper]
  3. Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and the Emulators
    Wenjia Zhao, Kangjie Lu, Qiushi Wu, and Yong Qi
    NDSS 2022 · Network and Distributed System Security Symposium [paper]

2021

  1. Detecting Missed Security Operations Through Differential Checking of Object-based Similar Paths
    Dinghao Liu, Qiushi Wu, Shouling Ji, Kangjie Lu, Zhenguang Liu, Jianhai Chen, and Qinming He
    CCS 2021 · ACM Conference on Computer and Communications Security [paper]
  2. Detecting Disordered Error Handling with Precise Function Pairing
    Qiushi Wu, Aditya Pakki, Navid Emamdoost, Stephen McCamant, and Kangjie Lu
    USENIX Security 2021 · USENIX Security Symposium [paper]
  3. Practically Detecting Kernel Memory Leaks in Specialized Modules and Beyond
    Navid Emamdoost, Qiushi Wu, Kangjie Lu, and Stephen McCamant
    NDSS 2021 · Network and Distributed System Security Symposium [paper]
  4. Unleashing Fuzzing Through Comprehensive, Efficient, and Faithful Exploitable-Bug Exposing
    Bowen Wang*, Kangjie Lu*, Qiushi Wu, and Aditya Pakki (*co-first authors)
    IEEE TDSC 2021 · IEEE Transactions on Dependable and Secure Computing · Journal [paper]

2020

  1. Precisely Characterizing Security Impact in a Flood of Patches via Symbolic Rule Comparison
    Qiushi Wu, Yang He, Stephen McCamant, and Kangjie Lu
    NDSS 2020 · Network and Distributed System Security Symposium [paper]

2019

  1. Detecting Missing-Check Bugs via Semantic- and Context-Aware Criticalness and Constraints Inferences
    Kangjie Lu, Aditya Pakki, and Qiushi Wu
    USENIX Security 2019 · USENIX Security Symposium [paper] [code]
  2. Automatically Identifying Security Checks for Detecting Kernel Semantic Bugs
    Kangjie Lu, Aditya Pakki, and Qiushi Wu
    ESORICS 2019 · European Symposium on Research in Computer Security [paper]

Software & Artifacts

  • BugStoneBench (first-authored, ICML 2026) — benchmark dataset accompanying “One Bug, Hundreds Behind,” for evaluating LLM-based large-scale bug discovery. [dataset]
  • GNNIC (first-authored, NDSS 2024) — refines indirect-call targets and kernel call graphs with abstract similarity.
  • SID (first-authored, NDSS 2020) — determines the security impact of patches via symbolic rule comparison.
  • HERO (first-authored, USENIX Security 2021) — detects disordered error-handling bugs with precise function pairing.
  • DIFFCVSS (co-first-authored, USENIX Security 2022) — prioritizes vulnerabilities via OS-aware differential severity analysis.
  • APILOT (co-authored, ACSAC 2025) — mitigates outdated, vulnerable API usage in LLM code suggestions; awarded ACSAC’s Code Reviewed badge. [code]
  • DualLM (co-authored, NDSS 2026) — detects security-critical Linux kernel patches, focusing on use-after-free and out-of-bounds vulnerabilities. [code]
  • CRIX (co-authored, USENIX Security 2019) — detects missing-check bugs in OS kernels. [code]
  • NDI (co-authored, CCS 2022) — a deterministic oracle for detecting security bugs. [code]
  • ClawStack — public experiments applying AI agents to real CTF-style security tasks; top 6% at BearcatCTF 2026 (40 of 44 challenges solved). [showcase]
  • Automated vulnerability discovery — contributed to program-analysis research that identified hundreds of confirmed security bugs in widely deployed open-source software, including the Linux kernel and OpenSSL.

Patents

  • Qiushi Wu, Zhongshu Gu, and Hani Jamjoom. “Indirect Function Call Target Identification in Software.” U.S. Patent 11,853,751.
  • Qiushi Wu, Zhongshu Gu, Enriquillo Valdez, and Hani Jamjoom. “Detecting Bugs Using Large Language Models.” U.S. Patent Application 2026/0017174 A1 (pending).

Teaching & Mentoring

  • CSCI 4271: Development of Secure Software Systems (University of Minnesota, Fall 2022) — Graduate Teaching Assistant; sole graduate TA for a class of approximately 40–60 students; designed hands-on labs, including a vulnerability-finding lab and an exploitation lab.
  • CSCI 2021: Machine Architecture and Organization (University of Minnesota, Fall 2018) — Graduate Teaching Assistant; one of two graduate TAs for 200+ enrolled students, coordinating a team of 5+ undergraduate TAs.
  • Mentoring — mentored a junior Ph.D. student through multiple co-authored publications (now a Research Scientist at Amazon); currently mentoring an IBM Research Ph.D. intern on the security of agentic systems and co-mentoring two first-year Ph.D. students on LLM/agentic methods for security.

Service

  • Program committee: IEEE Symposium on Security and Privacy (IEEE S&P), 2024; Network and Distributed System Security Symposium (NDSS), 2025.
  • Organizing committee: Workshop on Artificial Intelligence System with Confidential Computing (AISCC), co-located with NDSS, 2024.
  • External reviewer: ACM CCS 2019/2020; ICICS 2019; NDSS 2021.