Fuzzing the Kernel Using Unicornafl and AFL++

Overview

Unicorefuzz

Build Status code-style: black

Fuzzing the Kernel using UnicornAFL and AFL++. For details, skim through the WOOT paper or watch this talk at CCCamp19.

Is it any good?

yes.

AFL Screenshot

Unicorefuzz Setup

  • Install python2 & python3 (ucf uses python3, however qemu/unicorn needs python2 to build)
  • Run ./setup.sh, preferrably inside a Virtualenv (else python deps will be installed using --user). During install, afl++ and uDdbg as well as python deps will be pulled and installed.
  • Enjoy ucf

Upgrading

When upgrading from an early version of ucf:

  • Unicorefuzz will notify you of config changes and new options automatically.
  • Alternatively, run ucf spec to output a commented config.py spec-like element.
  • probe_wrapper.py is now ucf attach.
  • harness.py is now named ucf emu.
  • The song remains the same.

Debug Kernel Setup (Skip this if you know how this works)

  • Create a qemu-img and install your preferred OS on there through qemu
  • An easy way to get a working userspace up and running in QEMU is to follow the steps described by syzkaller, namely create-image.sh
  • For kernel customization you might want to clone your preferred kernel version and compile it on the host. This way you can also compile your own kernel modules (e.g. example_module).
  • In order to find out the address of a loaded module in the guest OS you can use cat /proc/modules to find out the base address of the module location. Use this as the offset for the function where you want to break. If you specify MODULE and BREAK_OFFSET in the config.py, it should use ./get_mod_addr.sh to start it automated.
  • You can compile the kernel with debug info. When you have compiled the linux kernel you can start gdb from the kernel folder with gdb vmlinux. After having loaded other modules you can use the lx-symbols command in gdb to load the symbols for the other modules (make sure the .ko files of the modules are in your kernel folder). This way you can just use something like break function_to_break to set breakpoints for the required functions.
  • In order to compile a custom kernel for Arch, download the current Arch kernel and set the .config to the Arch default. Then set DEBUG_KERNEL=y, DEBUG_INFO=y, GDB_SCRIPTS=y (for convenience), KASAN=y, KASAN_EXTRA=y. For convenience, we added a working example_config that can be place to the linux dir.
  • To only get necessary kernel modules boot the current system and execute lsmod > mylsmod and copy the mylsmod file to your host system into the linux kernel folder that you downloaded. Then you can use make LSMOD=mylsmod localmodconfig to only make the kernel modules that are actually needed by the guest system. Then you can compile the kernel like normal with make. Then mount the guest file system to /mnt and use make modules_install INSTALL_MOD_PATH=/mnt. At last you have to create a new initramfs, which apparently has to be done on the guest system. Here use mkinitcpio -k <folder in /lib/modules/...> -g <where to put initramfs>. Then you just need to copy that back to the host and let qemu know where your kernel and the initramfs are located.
  • Setting breakpoints anywhere else is possible. For this, set BREAKADDR in the config.py instead.
  • For fancy debugging, ucf uses uDdbg
  • Before fuzzing, run sudo ./setaflops.sh to initialize your system for fuzzing.

Run

  • ensure a target gdbserver is reachable, for example via ./startvm.sh
  • adapt config.py:
    • provide the target's gdbserver network address in the config to the probe wrapper
    • provide the target's target function to the probe wrapper and harness
    • make the harness put AFL's input to the desired memory location by adopting the place_input func config.py
    • add all EXITs
  • start ucf attach, it will (try to) connect to gdb.
  • make the target execute the target function (by using it inside the vm)
  • after the breakpoint was hit, run ucf fuzz. Make sure afl++ is in the PATH. (Use ./resumeafl.sh to resume using the same input folder)

Putting afl's input to the correct location must be coded invididually for most targets. However with modern binary analysis frameworks like IDA or Ghidra it's possible to find the desired location's address.

The following place_input method places at the data section of sk_buff in key_extract:

    # read input into param xyz here:
    rdx = uc.reg_read(UC_X86_REG_RDX)
    utils.map_page(uc, rdx) # ensure sk_buf is mapped
    bufferPtr = struct.unpack("<Q",uc.mem_read(rdx + 0xd8, 8))[0]
    utils.map_page(uc, bufferPtr) # ensure the buffer is mapped
    uc.mem_write(rdx, input) # insert afl input
    uc.mem_write(rdx + 0xc4, b"\xdc\x05") # fix tail

QEMUing the Kernel

A few general pointers. When using ./startvm.sh, the VM can be debugged via gdb. Use

$gdb
>file ./linux/vmlinux
>target remote :1234

This dynamic method makes it rather easy to find out breakpoints and that can then be fed to config.py. On top, startvm.sh will forward port 22 (ssh) to 8022 - you can use it to ssh into the VM. This makes it easier to interact with it.

Debugging

You can step through the code, starting at the breakpoint, with any given input. The fancy debugging makes use of uDdbg. To do so, run ucf emu -d $inputfile. Possible inputs to the harness (the thing wrapping afl-unicorn) that help debugging:

-d flag loads the target inside the unicorn debugger (uDdbg) -t flag enables the afl-unicorn tracer. It prints every emulated instruction, as well as displays memory accesses.

Gotchas

A few things to consider.

FS_BASE and GS_BASE

Unicorn did not offer a way to directly set model specific registers directly. The forked unicornafl version of AFL++ finally supports it. Most ugly code of earlier versions was scrapped.

Improve Fuzzing Speed

Right now, the Unicorefuzz ucf attach harness might need to be manually restarted after an amount of pages has been allocated. Allocated pages should propagate back to the forkserver parent automatically but might still get reloaded from disk for each iteration.

IO/Printthings

It's generally a good idea to nop out kprintf or kernel printing functionality if possible, when the program is loaded into the emulator.

Troubleshooting

If you got trouble running unicorefuzz, follow these rulse, worst case feel free to reach out to us, for example to @domenuk on twitter. For some notes on debugging and developing ucf and afl-unicorn further, read DEVELOPMENT.md

Just won't start

Run the harness without afl (ucf emu -t ./sometestcase). Make sure you are not in a virtualenv or in the correct one. If this works but it still crashes in AFL, set AFL_DEBUG_CHILD_OUTPUT=1 to see some harness output while fuzzing.

All testcases time out

Make sure ucf attach is running, in the same folder, and breakpoint has been triggered.

Owner
Security in Telecommunications
The Computer Security Group at Berlin University of Technology
Security in Telecommunications
Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

Luyu Gao 92 Jan 02, 2023
Demonstrational Session git repo for H SAF User Workshop (28/1)

5th H SAF User Workshop The 5th H SAF User Workshop supported by EUMeTrain will be held in online in January 24-28 2022. This repository contains inst

H SAF 4 Aug 04, 2022
A Python library for differentiable optimal control on accelerators.

A Python library for differentiable optimal control on accelerators.

Google 80 Dec 21, 2022
Generative Flow Networks for Discrete Probabilistic Modeling

Energy-based GFlowNets Code for Generative Flow Networks for Discrete Probabilistic Modeling by Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Vo

Narsil-Dinghuai Zhang 51 Dec 20, 2022
🤗 Push your spaCy pipelines to the Hugging Face Hub

spacy-huggingface-hub: Push your spaCy pipelines to the Hugging Face Hub This package provides a CLI command for uploading any trained spaCy pipeline

Explosion 30 Oct 09, 2022
Sudoku solver - A sudoku solver with python

sudoku_solver A sudoku solver What is Sudoku? Sudoku (Japanese: 数独, romanized: s

Sikai Lu 0 May 22, 2022
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images In this paper, we present an effective Dynamic Enhancement Anchor

13 Dec 09, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022
This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

Motion-Focused Contrastive Learning of Video Representations Introduction This is the code for the paper "Motion-Focused Contrastive Learning of Video

11 Sep 23, 2022
Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

this is a simple artificial neural network model using deep learning and torch-audio to classify cats and dog sounds.

crispengari 3 Dec 05, 2022
ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

Xtra Computing Group 1.4k Dec 22, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
A Python module for the generation and training of an entry-level feedforward neural network.

ff-neural-network A Python module for the generation and training of an entry-level feedforward neural network. This repository serves as a repurposin

Riadh 2 Jan 31, 2022
Official implementation of Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D usi

Visual Intelligence and Systems Group 441 Dec 20, 2022
🎃 Core identification module of AI powerful point reading system platform.

ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con

CrashKing 1 Jan 11, 2022
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

yueliu1999 297 Dec 27, 2022
113 Nov 28, 2022
Hippocampal segmentation using the UNet network for each axis

Hipposeg Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg Red: False Positive Gr

Juan Carlos Aguirre Arango 0 Sep 02, 2021
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 06, 2022
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022