DAFuzz:对内存数据存储的数据意识模糊化
Yingpei Zeng1, Fengming Zhu1, Siyi Zhang1
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China.
PeerJ. Computer science
|October 9, 2023
概括
数据意识模糊 (DAFuzz) 通过确保正确的数据使用来改善软件漏洞发现. 这种方法增强了代码路径覆盖范围,并加快了数据密集型应用程序中安全漏洞的识别.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 在软件漏洞检测中,fuzzing 非常重要.
- 现有的语法和语义意识的模糊化方法与内存数据存储扎.
- 某些代码路径需要特定的数据可用性,限制了当前的模糊效率.
研究的目的:
- 为内存数据存储引入一种新的数据意识的模糊化方法 (DAFuzz).
- 通过考虑输入生成和执行期间的数据使用来增强模糊化.
- 提高对数据敏感代码路径的覆盖范围.
主要方法:
- DAFuzz将各种数据加载到存储器中,然后将其加载到不同的代码路径中.
- 它产生语法和语义上有效的输入,正确地利用可用数据.
- 使用Superion实现了一个原型,并在Redis和Memcached上进行了测试.
主要成果:
- 与AFL,Superion,AFL++和AFLNet相比,DAFuzz实现了13-95%的更大的边缘覆盖.
- 使用DAFuzz,发现漏洞的速度超过2.7倍.
- 在Redis和Memcached中发现了四个新的漏洞.
结论:
- DAFuzz有效地解决了数据存储现有模糊化技术的局限性.
- 该方法显著提高了漏洞检测率和效率.
- 所有发现的漏洞都被开发人员报告,承认和修复.
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