deeso

Chat GPT "DAN" (and other "Jailbreaks")

• https://chat.openai.com/
• Is ChatGPT "DAN" Real? Gonna find out [Part 1]
  (https://www.youtube.com/watch?v=-q8woRG9FrI)
• Part 2: I thought ChatGPT DAN was a hoax, but...
  (https://www.youtube.com/watch?v=rHZRrDu3A2U&lc=UgxfrxX8aK7gnCzkend4AaABAg)

deeso

import pandas as pd
import numpy as np
from scipy.stats import beta, binom



def binomial_beta_mixture(probs: np.array, beta_0: np.array, beta_1: np.array, data: np.array):
    '''
    probs: 
    beta_0: shape of the first beta binomian distribution

deeso

deeso

version: '3'
services:
  indradb:
      build:
          context: .
          dockerfile: indradb.Dockerfile
      ports:
      - "27620:27615" # mongo db running here too

deeso

import json
import sys
import requests
from datetime import datetime, timedelta
from argparse import ArgumentParser


DAYS = 30
LAST_SEEN = (datetime.now() - timedelta(days=DAYS)).strftime("%Y-%m-%d")

deeso

# requires pymssql, sqlalchemy
import sys
import argparse
import pymssql
import json

from datetime import datetime, timedelta

from sqlalchemy.engine import create_engine, URL
from sqlalchemy.orm import sessionmaker

deeso

import requests
import sys
import random
import argparse
import json

url = "https://www.roblox.com/mobileapi/userinfo"

preface = "WARNING:-DO-NOT-SHARE-THIS.--Sharing-this-will-allow-someone-to-log-in-as-you-and-to-steal-your-ROBUX-and-items."
headers = {

deeso

Threat Model Checklist and Cheatsheet

Overview

Threat modeling is systems based analysis that identifies potential security short comings. Based on these analyses, design requirements are adopted to mitigate security risks. Furthermore, during the verification stages, explicit tests can be created to focus on or around issues surfaced by the threat model.

Threat models should be performed

1. A product is created
2. A new feature is released
3. Security incident occurs
4. Architectural or infrastructure changes

deeso

import json
import sys
import os
import boto3

import argparse

parser = argparse.ArgumentParser(description='Easy Fng Button for AWS Role Gneration.')

parser.add_argument('-aws_name', type=str, default='UNKNOWN_NAME',

deeso

import ldap3

password = ''
host_ip = ""
user_dn = "cn=Allen,OU=abc,OU=efgh,OU=zyx,DC=fqdg,DC=internal"  

USERNAME = 'username'
DISPLAY_NAME = 'displayName'
MANAGER = 'manager'
NAME = 'name'