most citedLLM4Fuzz: Guided Fuzzing of Smart Contracts with Large Language Models

8 citations · 8 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SE2026

TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution

Shuangjie Yao, Hao Wang, Koushik Sen +3

Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all existing benchmarks still rely on t…

cs.RO2026

Building Trustworthy Household Robots through Open-World Safety

Yu-Lin Tsai, Muxi Lyu, Roei Herzig +3

Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in complex environments. These ad…

cs.CR2026

RECEIPT: Deterministic, Reward-Hacking-Resistant Verification for White-Box Agentic XSS Discovery

Muxi Lyu, Karen Shieh, Yiwei Hou +3

Cross-Site Scripting (XSS) remains one of the most prevalent and damaging classes of web vulnerabilities. LLM-based coding agents offer a promising approach to XSS discovery by com…

cs.CR2026

Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases

Yiwei Hou, Hao Wang, Muxi Lyu +6

Memory safety vulnerabilities remain a significant threat even for projects with extensive fuzzing and manual auditing. Recent results suggest that large language models hold great…

cs.AI2026

Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack

Hao Wang, Hanchen Li, Qiuyang Mang +3

Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a s…

cs.CR2024

BACKRUNNER: Mitigating Smart Contract Attacks in the Real World

Chaofan Shou, Yuanyu Ke, Yupeng Yang +11

Billions of dollars have been lost due to vulnerabilities in smart contracts. To counteract this, researchers have proposed attack frontrunning protections designed to preempt mali…

cs.CR2024★ 8 cited

LLM4Fuzz: Guided Fuzzing of Smart Contracts with Large Language Models

Chaofan Shou, Jing Liu, Doudou Lu +1

As blockchain platforms grow exponentially, millions of lines of smart contract code are being deployed to manage extensive digital assets. However, vulnerabilities in this mission…