Dytan

Dytan: a generic dynamic taint analysis framework. Dynamic taint analysis is gaining momentum. Techniques based on dynamic tainting have been successfully used in the context of application security, and now their use is also being explored in different areas, such as program understanding, software testing, and debugging. Unfortunately, most existing approaches for dynamic tainting are defined in an ad-hoc manner, which makes it difficult to extend them, experiment with them, and adapt them to new contexts. Moreover, most existing approaches are focused on data-flow based tainting only and do not consider tainting due to control flow, which limits their applicability outside the security domain. To address these limitations and foster experimentation with dynamic tainting techniques, we defined and developed a general framework for dynamic tainting that (1) is highly flexible and customizable, (2) allows for performing both data-flow and control-flow based tainting conservatively, and (3) does not rely on any customized run-time system. We also present DYTAN, an implementation of our framework that works on x86 executables, and a set of preliminary studies that show how DYTAN can be used to implement different tainting-based approaches with limited effort. In the studies, we also show that DYTAN can be used on real software, by using FIREFOX as one of our subjects, and illustrate how the specific characteristics of the tainting approach used can affect efficiency and accuracy of the taint analysis, which further justifies the use of our framework to experiment with different variants of an approach.


References in zbMATH (referenced in 6 articles )

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  1. Ferrara, Pietro; Olivieri, Luca; Spoto, Fausto: \textsfBackFlow: backward context-sensitive flow reconstruction of taint analysis results (2020)
  2. Cheng, Xiaoyang; Lin, Yan; Gao, Debin; Jia, Chunfu: DynOpVm: VM-based software obfuscation with dynamic opcode mapping (2019)
  3. Sahabandu, Dinuka; Moothedath, Shana; Allen, Joey; Bushnell, Linda; Lee, Wenke; Poovendran, Radha: Stochastic dynamic information flow tracking game with reinforcement learning (2019)
  4. Cai, Jun; Zou, Peng; Ma, Jinxin; He, Jun: SwordDTA: A dynamic taint analysis tool for software vulnerability detection (2016) ioport
  5. Zhu, Haiyan; Dillig, Thomas; Dillig, Isil: Automated inference of library specifications for source-sink property verification (2013)
  6. Zhang, Ruoyu; Huang, Shiqiu; Qi, Zhengwei; Guan, Haibing: Static program analysis assisted dynamic taint tracking for software vulnerability discovery (2012) ioport