论文标题
污染兔子：通过动态快速路径生成优化通用污点分析
The Taint Rabbit: Optimizing Generic Taint Analysis with Dynamic Fast Path Generation
论文作者
论文摘要
通用污染分析是软件安全性的关键技术。但是，它遭受了惊人的头顶。在本文中，我们探讨了及时（JIT）跟踪污点的快速路径是否可以增强性能的假设。为此，我们介绍了污点，该兔子支持高度可定制的用户定义的污点策略，并将JIT与快速上下文切换结合在一起。我们的实验结果表明，这种组合的表现超过了广泛的污染分析的现有实现，并将性能差距弥补了专用跟踪器。例如，Dytan的平均开销为237倍，而Taint Rabbit在相同的基准标准上达到1.7倍。这与位于非生成，污点发动机libdft交付的1.5倍开销相比。
Generic taint analysis is a pivotal technique in software security. However, it suffers from staggeringly high overhead. In this paper, we explore the hypothesis whether just-in-time (JIT) generation of fast paths for tracking taint can enhance the performance. To this end, we present the Taint Rabbit, which supports highly customizable user-defined taint policies and combines a JIT with fast context switching. Our experimental results suggest that this combination outperforms notable existing implementations of generic taint analysis and bridges the performance gap to specialized trackers. For instance, Dytan incurs an average overhead of 237x, while the Taint Rabbit achieves 1.7x on the same set of benchmarks. This compares favorably to the 1.5x overhead delivered by the bitwise, non-generic, taint engine LibDFT.
