一句话：指出现有 fuzzing 能量分配假设属性效率跨程序一致的问题，并提出程序敏感的能量分配方案 SLIME。
该论文关注模糊测试中的能量分配策略：其核心思想是向更容易在变异后触发新路径或崩溃的种子分配更多计算能量。现有方案常以执行速度、文件大小、控制流图触发的边数等属性作为种子潜力度量，并隐含假设这些属性的效率在不同程序间相同。作者发现该假设并不总是成立，因此采用静态能量分配逻辑的最先进方案难以在不同程序上取得理想性能。论文据此提出程序敏感的能量分配方法 SLIME，使分配逻辑能够适应具体程序；但所给摘要未进一步说明其具体机制与实验评估结果。
The energy allocation strategy is one of the most popular techniques in fuzzing to improve code coverage and vulnerability discovery. The core intuition is that fuzzers should allocate more computational energy to the seed files that have high efficiency to trigger unique paths and crashes after mutation. Existing solutions usually define several properties, e.g., the execution speed, the file size, and the number of the triggered edges in the control flow graph, to serve as the key measurements in their allocation logics to estimate the potential of a seed. The efficiency of a property is usually assumed to be the same across different programs. However, we find that this assumption is not always valid. As a result, the state-of-the-art energy allocation solutions with static energy allocation logics are hard to achieve desirable performance on different programs.
DOI 原文 ·
@inproceedings{paperbot7428,
title = {SLIME: program-sensitive energy allocation for fuzzing},
author = {Chenyang Lyu and Hong Liang and Shouling Ji and Xuhong Zhang and Binbin Zhao and Meng Han and Yun Li and Zhe Wang and Wenhai Wang and Raheem Beyah},
booktitle = {ISSTA},
year = {2022},
doi = {10.1145/3533767.3534385}
}