QilingLab challenge writeup

Overview

qiling lab writeup

shielder 在 2021/7/21 發布了 QilingLab 來幫助學習 qiling framwork 的用法,剛好最近有用到,順手解了一下並寫了一下 writeup。

前情提要

Qiling 是一款功能強大的模擬框架,和 qemu user mode 類似,但可以做到更多功能,詳情請見他們的 github網站

他們有官方文件,解此題目前建議看一下。

我所解的為 aarch64 的 challenge,使用的 rootfs 為 qililng 所提供的 arm64_linux

逆向工具用 ghidra,因為我沒錢買 idapro。

First

先隨手寫個 python 用 qiling 執行 challenge binary。

import sys
from qiling import *
from qiling.const import QL_VERBOSE

sys.path.append("..")


if __name__ == "__main__":
    ql = Qiling(["qilinglab-aarch64"], "rootfs/arm64_linux",verbose=QL_VERBOSE.OFF)
    ql.run()

可以看到結果是 binary 會不正常執行,此為正常現象,有些 Challenge 沒解完會導致錯誤或是無窮迴圈。

Welcome to QilingLab.
Here is the list of challenges:
Challenge 1: Store 1337 at pointer 0x1337.
Challenge 2: Make the 'uname' syscall return the correct values.
Challenge 3: Make '/dev/urandom' and 'getrandom' "collide".
Challenge 4: Enter inside the "forbidden" loop.
Challenge 5: Guess every call to rand().
Challenge 6: Avoid the infinite loop.
Challenge 7: Don't waste time waiting for 'sleep'.
Challenge 8: Unpack the struct and write at the target address.
Challenge 9: Fix some string operation to make the iMpOsSiBlE come true.
Challenge 10: Fake the 'cmdline' line file to return the right content.
Challenge 11: Bypass CPUID/MIDR_EL1 checks.

Checking which challenge are solved...
Note: Some challenges will results in segfaults and infinite loops if they aren't solved.
[x]	

[x]	x0	:	 0x0
[x]	x1	:	 0x0
[x]	x2	:	 0x1
[x]	x3	:	 0x0
[x]	x4	:	 0x0

Challenge 1

把 0x1337 的位置的值改成 1337

用 qiling 把該位置的 memory 讀出來,在進行改寫,要注意 align 問題。詳情請見文件

    ql.mem.map(0x1337//4096*4096, 4096)
    ql.mem.write(0x1337,ql.pack16(1337) )

Challenge 2

改掉 uname 此 system call 的 return。

可以看到他去比對 uname.sysname 和 uname.version 是否為特定值。我採用對 system call 進行 hijack

去翻 linux 文件 可以看到 uname 回傳的格式為 :

struct utsname {
               char sysname[];    /* Operating system name (e.g., "Linux") */
               char nodename[];   /* Name within "some implementation-defined
                                     network" */
               char release[];    /* Operating system release
                                     (e.g., "2.6.28") */
               char version[];    /* Operating system version */
               char machine[];    /* Hardware identifier */
           #ifdef _GNU_SOURCE
               char domainname[]; /* NIS or YP domain name */
           #endif
};

依照此文件把相對應的位置改掉。注意如果 release 改太小或是沒給,會噴錯。

def my_syscall_uname(ql, write_buf, *args, **kw):
    buf = b'QilingOS\x00' # sysname
    ql.mem.write(write_buf, buf)
    buf = b'30000'.ljust(65, b'\x00') # important!! If not sat will `FATAL: kernel too old`
    ql.mem.write(write_buf+65*2, buf)
    buf = b'ChallengeStart'.ljust(65, b'\x00') # version
    ql.mem.write(write_buf+65*3, buf)
    regreturn = 0
    return regreturn

ql.set_syscall("uname", my_syscall_uname)

Challenge 3

/dev/random,從中讀取兩次,確保第一次的值和 getrandom 得到的值相同,且其中沒有第二次讀到值。

查了一下 getrandom 是一 system call。因此對 /dev/random 和 getrandom() 進行 hijack 即可

class Fake_urandom(QlFsMappedObject):
    def read(self, size):
        if(size > 1):
            return b"\x01" * size
        else:
            return b"\x02"
    def fstat(self): # syscall fstat will ignore it if return -1
        return -1
    def close(self):
        return 0

def my_syscall_getrandom(ql, write_buf, write_buf_size, flag , *args, **kw):
    buf = b"\x01" * write_buf_size
    ql.mem.write(write_buf, buf)
    regreturn = 0
    return regreturn
    
ql.add_fs_mapper('/dev/urandom', Fake_urandom())
ql.set_syscall("getrandom", my_syscall_getrandom)

Challenge 4

進入不能進去的迴圈

直接 hook cmp 的位置讓 reg w0 是 1 即可,位置記得要加上 pie。

    # 00100fd8 e0 1b 40 b9     ldr        w0,[sp, #local_8]
    # 00100fdc e1 1f 40 b9     ldr        w1,[sp, #local_4]
    # 00100fe0 3f 00 00 6b     cmp        w1,w0    <- hook         
def hook_cmp(ql):
    ql.reg.w0 = 1
    return

base_addr = ql.mem.get_lib_base(ql.path) # get pie_base addr
ql.hook_address(hook_cmp, base_addr + 0xfe0)

Challenge 5

rand() 出來的值和 0 比較要通過

直接 hijack rand() 讓他回傳都是 0 即可。

def hook_cmp(ql):
    ql.reg.w0 = 1
    return
    
ql.set_api("rand", hook_rand)

Challenge 6

解開無窮迴圈

和 Challenge 4 同想法,hook cmp。

def hook_cmp2(ql):
    ql.reg.w0 = 0
    return
    
ql.hook_address(hook_cmp2, base_addr + 0x001118)

Challenge 7

不要讓他 sleep。 解法很多,可以 hook sleep 這個 api,或是看 sleep linux 文件能知道內部處理是用 nanosleep,hook 他即可。

def hook_sleeptime(ql):
    ql.reg.w0 = 0
    return
ql.hook_address(hook_sleeptime, base_addr + 0x1154)

Challenge 8

裡面最難的一題,他是建立特殊一個結構長這個樣子。

struct something(0x18){ 
 string_ptr -> malloc (0x1e) ->  0x64206d6f646e6152
 long_int = 0x3DFCD6EA00000539
 check_addr -> check;
}  

由於他結構內部有 0x3DFCD6EA00000539 這個 magic byte,因此可以直接對此作搜尋並改寫內部記憶體。這邊要注意搜尋可能找到其他位置,因此前面可以加對 string_ptr 所在位置的判斷。

def find_and_patch(ql, *args, **kw):
    MAGIC = 0x3DFCD6EA00000539
    magic_addrs = ql.mem.search(ql.pack64(MAGIC)) 

    # check_all_magic
    for magic_addr in magic_addrs:
        # Dump and unpack the candidate structure
        malloc1_addr = magic_addr - 8
        malloc1_data = ql.mem.read(malloc1_addr, 24)
        # unpack three unsigned long
        string_addr, _ , check_addr = struct.unpack('QQQ', malloc1_data)

        # check string data        
        if ql.mem.string(string_addr) == "Random data":
            ql.mem.write(check_addr, b"\x01")
            break
    return
    
ql.hook_address(find_and_patch, base_addr + 0x011dc)

另一種解法則是由於該結構在 stack 上,因此直接讀 stack 即可。

Challenge 9

把一字串轉用tolower小寫,再用 strcmp 比較。

解法一樣很多種,我是 hijack tolower() 讓他啥事都不做。

def hook_tolower(ql):
    return
    
ql.set_api("tolower", hook_tolower)

Challenge 10

打開不存在的文件,讀取的值需要是 qilinglab

和 Challenge 3 作法一樣,這邊要注意的是 return 要是 byte,string 會出錯。 = =

class Fake_cmdline(QlFsMappedObject):

    def read(self, size):
        return b"qilinglab" # type should byte byte, string will error = =
    def fstat(self): # syscall fstat will ignore it if return -1
        return -1
    def close(self):
        return 0

ql.add_fs_mapper('/proc/self/cmdline', Fake_cmdline())

Challenge 11

可以看到他從 MIDR_EL1 取值,而此為特殊的暫存器。

這邊解法是去 hook code,我選擇 hook 這段

# 001013ec 00 00 38 d5     mrs        x0,midr_el1

去搜尋所有記憶體為 b"\x00\x00\x38\xD5" ,讓他執行時把 x0 暫存器改寫,並更改 pc。

def midr_el1_hook(ql, address, size):  
    if ql.mem.read(address, size) == b"\x00\x00\x38\xD5":
        # if any code is mrs        x0,midr_el1
        # Write the expected value to x0
        ql.reg.x0 = 0x1337 << 0x10
        # Go to next instruction
        ql.reg.arch_pc += 4
    # important !! Maybe hook library
    # see : https://joansivion.github.io/qilinglabs/
    return

ql.hook_code(midr_el1_hook)

Done

Thanks

Thanks MANSOUR Cyril release his writeup, help me alot.

Owner
Yuan
Yuan
Code, Data and Demo for Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting

InversePrompting Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting Code: The code is provided in the "chinese_ip"

THUDM 101 Dec 16, 2022
The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

Yabin Zhang 26 Dec 26, 2022
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Jacob 27 Oct 23, 2022
【steal piano】GitHub偷情分析工具!

【steal piano】GitHub偷情分析工具! 你是否有这样的困扰,有一天你的仓库被很多人加了star,但是你却不知道这些人都是从哪来的? 别担心,GitHub偷情分析工具帮你轻松解决问题! 原理 GitHub偷情分析工具透过分析star的时间以及他们之间的follow关系,可以推测出每个st

黄巍 442 Dec 21, 2022
DAN: Unfolding the Alternating Optimization for Blind Super Resolution

DAN-Basd-on-Openmmlab DAN: Unfolding the Alternating Optimization for Blind Super Resolution We reproduce DAN via mmediting based on open-sourced code

AlexZou 72 Dec 13, 2022
Semantic Segmentation Suite in TensorFlow

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

George Seif 2.5k Jan 06, 2023
"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021

undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f

Yichen Jiang 0 Mar 25, 2022
MT3: Multi-Task Multitrack Music Transcription

MT3: Multi-Task Multitrack Music Transcription MT3 is a multi-instrument automatic music transcription model that uses the T5X framework. This is not

Magenta 867 Dec 29, 2022
VLG-Net: Video-Language Graph Matching Networks for Video Grounding

VLG-Net: Video-Language Graph Matching Networks for Video Grounding Introduction Official repository for VLG-Net: Video-Language Graph Matching Networ

Mattia Soldan 25 Dec 04, 2022
Brain Tumor Detection with Tensorflow Neural Networks.

Brain-Tumor-Detection A convolutional neural network model built with Tensorflow & Keras to detect brain tumor and its different variants. Data of the

404ErrorNotFound 5 Aug 23, 2022
Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

Yahui Liu 112 Dec 25, 2022
Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Wav2CLIP 🚧 WIP 🚧 Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP 📄 🔗 Ho-Hsiang Wu, Prem Seetharaman

Descript 240 Dec 13, 2022
Anchor-free Oriented Proposal Generator for Object Detection

Anchor-free Oriented Proposal Generator for Object Detection Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang, Yanqing Yao, Junwei Han, Intro

jbwang1997 56 Nov 15, 2022
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

1 Jan 23, 2022
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.

Optimized Einsum Optimized Einsum: A tensor contraction order optimizer Optimized einsum can significantly reduce the overall execution time of einsum

Daniel Smith 653 Dec 30, 2022
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
An end-to-end machine learning library to directly optimize AUC loss

LibAUC An end-to-end machine learning library for AUC optimization. Why LibAUC? Deep AUC Maximization (DAM) is a paradigm for learning a deep neural n

Andrew 75 Dec 12, 2022
It's final year project of Diploma Engineering. This project is based on Computer Vision.

Face-Recognition-Based-Attendance-System It's final year project of Diploma Engineering. This project is based on Computer Vision. Brief idea about ou

Neel 10 Nov 02, 2022
UCSD Oasis platform

oasis UCSD Oasis platform Local project setup Install Docker Compose and make sure you have Pip installed Clone the project and go to the project fold

InSTEDD 4 Jun 16, 2021
Inkscape extensions for figure resizing and editing

Academic-Inkscape: Extensions for figure resizing and editing This repository contains several Inkscape extensions designed for editing plots. Scale P

192 Dec 26, 2022