tianyuzhou95 / 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.

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

Requirement:

• Ubuntu 16.04, 18.04, 20.04
• Python3
  • 3rd party packages:networkx, matploitlib, 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

2. 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

3. the actual analysis

• Run kubo : python main.py run
• generate bug reports : python main.py stat

4. see ./work/bugs for the bug reports

Credit:

This work is built on other amazing works specifically

• For call graph analysis: https://github.com/lzto/pex
• For slicing and symbolic execution: https://github.com/sslab-gatech/deadline/
• For taint analysis: https://github.com/ucsb-seclab/dr_checker

Shout out to their amazong 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 [e-mail]. It's intended that this project to be actively maintained for a period of time, mainly for readability improvement and performance fine-tuning.