3 citations · 3 across the 6 of their papers we have counts for
6 papers
FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs
Ze Sheng, Aleksandar Kezic, Zhicheng Chen +1
Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate this capability by asking the mod…
Entropy-based Code Adversarial Translation for Real-world Repository Migration
Yushun Tang, Yisen Cao, Zhicheng Chen +4
LLMs have demonstrated strong capabilities in code generation and automated program repair, but migrating an entire repository rarely produces a runnable application because long-h…
Quality-Assured Fuzz Harness Generation via the Four Principles Framework
Ze Sheng, Dmitrijs Trizna, Luigino Camastra +3
Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs th…
FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction
Ze Sheng, Zhicheng Chen, Qingxiao Xu +2
Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise for automated vulnerability detec…
A Systematic Study of LLM-Based Architectures for Automated Patching
Qingxiao Xu, Ze Sheng, Zhicheng Chen +1
Large language models (LLMs) have shown promise for automated patching, but their effectiveness depends strongly on how they are integrated into patching systems. While prior work…
LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights
Ze Sheng, Zhicheng Chen, Shuning Gu +3
Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection, addressing critical challenges in the security domain. Traditional methods,…