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Haoran Xi
Observed name:
Xi, Haoran
Institutions:
Beijing University of Posts and Telecommunications,
New York University
Observed institutions:
Beijing University of Posts and Telecommunications,
New York University
ORCID:
0009-0005-3268-7366
Observed ORCIDs:
0009-0005-3268-7366,
0009-0009-3051-1630
OpenAlex ID:
A5101377025
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
2024
·
Abramovich, Talor
, Udeshi, Meet
, et al.
·
arXiv (Cornell University)
·
70
PDF
ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software
2026
·
Xiang Mei
, Jordi Del Castillo
, et al.
·
IEEE European Symposium on Security and Privacy
·
52
PDF
D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
2025
·
Udeshi, Meet
, Shao, Minghao
, et al.
·
arXiv (Cornell University)
·
29
PDF
Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark
2026
·
Minghao Shao
, Nanda Rani
, et al.
·
Proceedings of the AAAI Conference on Artificial Intelligence
·
13
From Trace to Line: An Empirical Study of What Drives LLM-Based OSS Vulnerability Localization
2026
·
Haoran Xi
, Minghao Shao
, et al.
·
IEEE Access
·
7
How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation
2026
·
Milner, Kimberly
, Shao, Minghao
, et al.
·
arXiv (Cornell University)
·
4
PDF
CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
2026
·
Rani, Nanda
, Milner, Kimberly
, et al.
·
arXiv (Cornell University)
·
4
PDF
AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes
2026
·
Xi, Haoran
, Shao, Minghao
, et al.
·
arXiv (Cornell University)
·
1
PDF
Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning
2026
·
Junhui Yin
, Yuchen Yang
, et al.
·
ACM International Conference on Multimedia (ACM MM)
460 - Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark
2026
·
Association for Artificial Intelligence 2026
, Aggarwal, Saksham
, et al.
·
Underline Science Inc.
Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark
2026
·
Association for Artificial Intelligence 2026
, Aggarwal, Saksham
, et al.
·
Underline Science Inc.
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Works count:
13
Citation count:
307
H-index:
7
I10-index:
6
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