6 papers
"Tab, Tab, Bug": Security Pitfalls of Next Edit Suggestions in AI-Integrated IDEs
Yunlong Lyu, Yixuan Tang, Peng Chen +4
Modern AI-integrated IDEs are shifting from passive code completion to proactive Next Edit Suggestions (NES). Unlike traditional autocompletion, NES is designed to construct a rich…
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
Hongxiang Zhang, Yuyang Rong, Yifeng He +1
Greybox fuzzing has achieved success in revealing bugs and vulnerabilities in programs. However, randomized mutation strategies have limited the fuzzer's performance on structured…
IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation
Yuyang Rong, Zhanghan Yu, Zhenkai Weng +2
Modern compilers, such as LLVM, are complex pieces of software. Due to their complexity, manual testing is unlikely to suffice, yet formal verification is difficult to scale. End-t…
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
Yifeng He, Jicheng Wang, Yuyang Rong +1
Testing is essential to modern software engineering for building reliable software. Given the high costs of manually creating test cases, automated test case generation, particular…
VulBinLLM: LLM-powered Vulnerability Detection for Stripped Binaries
Nasir Hussain, Haohan Chen, Chanh Tran +6
Recognizing vulnerabilities in stripped binary files presents a significant challenge in software security. Although some progress has been made in generating human-readable inform…
EthCluster: An Unsupervised Static Analysis Method for Ethereum Smart Contract
Hong-Sheng Huang, Jen-Yi Ho, Hao-Wen Chen +1
Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a…