DECAF: A Platform-Neutral Whole-System Dynamic Binary Analysis Platform

Andrew Henderson, Lok Kwong Yan, Xunchao Hu, Aravind Prakash, Heng Yin, Stephen McCamant

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Dynamic binary analysis is a prevalent and indispensable technique in program analysis. While several dynamic binary analysis tools and frameworks have been proposed, all suffer from one or more of: prohibitive performance degradation, a semantic gap between the analysis code and the program being analyzed, architecture/OS specificity, being user-mode only, and lacking APIs. We present DECAF, a virtual machine based, multi-target, whole-system dynamic binary analysis framework built on top of QEMU. DECAF provides Just-In-Time Virtual Machine Introspection and a plugin architecture with a simple-to-use event-driven programming interface. DECAF implements a new instruction-level taint tracking engine at bit granularity, which exercises fine control over the QEMU Tiny Code Generator (TCG) intermediate representation to accomplish on-the-fly optimizations while ensuring that the taint propagation is sound and highly precise. We perform a formal analysis of DECAF's taint propagation rules to verify that most instructions introduce neither false positives nor false negatives. We also present three platform-neutral plugins - Instruction Tracer, Keylogger Detector, and API Tracer, to demonstrate the ease of use and effectiveness of DECAF in writing cross-platform and system-wide analysis tools. Implementation of DECAF consists of 9,550 lines of C++ code and 10,270 lines of C code and we evaluate DECAF using CPU2006 SPEC benchmarks and show average overhead of 605 percent for system wide tainting and 12 percent for VMI.

Original languageEnglish (US)
Article number7506264
Pages (from-to)164-184
Number of pages21
JournalIEEE Transactions on Software Engineering
Issue number2
StatePublished - Feb 1 2017

Bibliographical note

Funding Information:
This research was supported in part by US National Science Foundation Grant #1018217, US National Science Foundation Grant #1054605, and McAfee Inc.


  • Dynamic binary analysis
  • dynamic taint analysis
  • virtual machine introspection

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