1 citations · 3 across the 13 of their papers we have counts for
14 papers
Usability as a Weapon: Attacking the Safety of LLM-Based Code Generation via Usability Requirements
Yue Li, Xiao Li, Hao Wu +5
Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices critical. In practice, however, many…
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…
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…
ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis
Hanzhong Liang, Yue Duan, Xing Su +5
As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain ne…
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…
LLM-based Human-like Traffic Simulation for Self-driving Tests
Wendi Li, Hao Wu, Han Gao +3
Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most…
When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs
Yue Li, Xiao Li, Hao Wu +4
Large Language Models (LLMs) have become integral to automated code analysis, enabling tasks such as vulnerability detection and code comprehension. However, their integration intr…
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…
Privacy-Preserving Federated Learning via Dataset Distillation
ShiMao Xu, Xiaopeng Ke, Xing Su +4
Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the kno…
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…
Making Every Frame Matter: Continuous Activity Recognition in Streaming Video via Adaptive Video Context Modeling
Hao Wu, Donglin Bai, Shiqi Jiang +6
Video activity recognition has become increasingly important in robots and embodied AI. Recognizing continuous video activities poses considerable challenges due to the fast expans…
Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?
Likun Zhang, Hao Wu, Lingcui Zhang +4
The emergence of text-to-image models has recently sparked significant interest, but the attendant is a looming shadow of potential infringement by violating the user terms. Specif…
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation
Hao Wu, Likun Zhang, Shucheng Li +2
In the federated learning (FL) process, since the data held by each participant is different, it is necessary to figure out which participant has a higher contribution to the model…
Unleashing the Power of LLM to Infer State Machine from the Protocol Implementation
Haiyang Wei, Ligeng Chen, Zhengjie Du +7
State machines are essential for enhancing protocol analysis to identify vulnerabilities. However, inferring state machines from network protocol implementations is challenging due…