/kubo

Use on-demand control- data- flow slicing combined with taint analysis and symbolic execution to produce scalable and precise UB detection for Linux kernel.

Primary LanguageC++

drawing

KUBO: precise and scalable static UB detector for the OS Kernel

Requirement:

  • Ubuntu 16.04, 18.04, 20.04
  • Python3
    • 3rd party packages:networkx, matplotlib, argparse, termcolor,ipython
  • cmake

Init

  • LLVM 9.0 : cd llvm && ./init.sh
    • this will prepare a pre-built as well as a natively built llvm 9.0 since we modify some of the source code
  • All sorts of dependent projects : cd deps && ./build.sh
  • KUBO pass : cd work && python llvm.py build -c

Kernel

  1. prepare
  • Download Linux Source : python main.py checkout(default to 5.4.1)
  • Config Linux Source : python main.py config
  • Build the linux binary : python main.py build
  • Parse build procedure : python main.py parse
  • Build llvm bc : python main.py irgen
  • Group into modules : python main.py group
  • Optimize and LTO : python main.py trans
  • Generate call graph : python main.py gen_cg
  • syscall/ioctl entry analysis : python main.py entry_ana
  • data summary generation : python main.py taint_ana
  1. generate bc with debug symbol so that we can map reported bugs to source code automatically when generating the bug reports
  • Build llvm bc : python main.py gen_dbg_ir
  • Group into modules : python main.py gen_dbg_group
  • Optimize and LTO : python main.py gen_dbg_trans
  1. the actual analysis
  • Run kubo : python main.py run
  • generate bug reports : python main.py stat
  1. see ./work/bugs for the bug reports

Credit:

This work is built on other amazing works specifically

Shout out to their amazing contributions that made this possible.

Q & A

Should you have any question, feel free to raise an issue in this repo or directly contact the author at liu.changm@northeastern.edu. It's intended that this project to be actively maintained for a period of time, mainly for readability improvement and performance fine-tuning.