activity
20222026
most citedTraining Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?

1 citations · 2 across the 9 of their papers we have counts for

collaborators
Showing cs.CRShow all

5 papers · 1 filter

cs.CR2026

From Transactions to Exploits: Automated PoC Synthesis for Real-World DeFi Attacks

Xing Su, Hao Wu, Hanzhong Liang +4

Blockchain systems are increasingly targeted by on-chain attacks that exploit contract vulnerabilities to extract value rapidly and stealthily, making systematic analysis and repro…

cs.CR2025

A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection

Xiao Li, Yue Li, Hao Wu +4

As large language models (LLMs) are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse vulnerability types have become a pressin…

cs.CR2025

Revealing Adversarial Smart Contracts through Semantic Interpretation and Uncertainty Estimation

Yating Liu, Xing Su, Hao Wu +4

Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts for financial gain. Detecting…

cs.CR20251 cited

Everything You Wanted to Know About LLM-based Vulnerability Detection But Were Afraid to Ask

Yue Li, Xiao Li, Hao Wu +5

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a cri…

cs.CR2024

If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization

Yue Li, Xiao Li, Hao Wu +5

The rapid expansion of software systems and the growing number of reported vulnerabilities have emphasized the importance of accurately identifying vulnerable code segments. Tradit…