6 papers
The History Is the Detector: Executing CVE Patch History, End-to-End
Qiushi Wu, Kevin Eykholt, Youngja Park +4
Public vulnerability databases collect rich information about known software flaws, including their weakness types, affected components, and related patches. Fixing commits provide…
How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment
Rui Zhu, Weiheng Bai, Qiushi Wu +3
Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encompassing frameworks like RLHF a…
AFLGopher: Accelerating Directed Fuzzing via Feasibility-Aware Guidance
Weiheng Bai, Kefu Wu, Qiushi Wu +1
Directed fuzzing is a useful testing technique that aims to efficiently reach target code sites in a program. The core of directed fuzzing is the guiding mechanism that directs the…
One Bug, Hundreds Behind: LLMs for Large-Scale Bug Discovery
Qiushi Wu, Yue Xiao, Dhilung Kirat +3
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project maintainers, who work with the repo…
What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs
Xingyu Li, Juefei Pu, Yifan Wu +11
Open-source software projects are foundational to modern software ecosystems, with the Linux kernel standing out as a critical exemplar due to its ubiquity and complexity. Although…
APILOT: Navigating Large Language Models to Generate Secure Code by Sidestepping Outdated API Pitfalls
Weiheng Bai, Keyang Xuan, Pengxiang Huang +4
With the rapid development of large language models (LLMs), their applications have expanded into diverse fields, such as code assistance. However, the substantial size of LLMs mak…