Autosploit: Advanced Remote Host Mass Exploitation

With this tool, you can easily launch an attack on a remote host within a fairly short time. This is made possible due to the availability of Shodan, a powerful search engine that allows you to automatically fish out targets that are connected to a particular network service. Alternatively, you can also use target seeking tools such as Zoomeye and Censys to search out intended targets.

Apart from the automated host searching and collection, AutoSploit also gives you the option of creating your own customized target list. With this option in place, you can effectively launch attack-intended searches on hosts of your choice by manually adding them to your list.

Metasploit Modules and How They Work Together

AutoSploit Features:

• Automated Target Collection
• Customized Target List (allows you to add your own list of targets)
• Metasploit Modules
• Custom user-agent
• Mass exploitations

Supported Platforms:

• Linux
• OS X (must be within virtual environments to properly function)

Dependencies:

This tool relies on the below Python 2.7 modules:

• requests
• psutil

The required dependencies should all be in place after performing an installation with the recommended method, but you can easily install them using pip:

$ pip install -r requirements.txt

Alternatively:

$ pip install requests psutil

Autosploit Install

Install AutoSploit via Docker Compose:

Clone the repo:

$ git clone https://github.com/NullArray/AutoSploit.git

Navigate to the Autosploit directory and run:

$ cd Autosploit/Docker
$ docker-compose run --rm autosploit

Install AutoSploit on Linux (via cloning)

Clone:

$ git clone https://github.com/NullArray/AutoSploit

Navigate to the AutoSploit directory, make the install script executable and install:

$ cd AutoSploit
$ chmod +x install.sh
$ ./install.sh

Usage

To start AutoSploit run:

$ python autosploit.py

This will take you to the available user options that you can choose from.

usage: python autosploit.py -[c|z|s|a] -[q] QUERY
                            [-C] WORKSPACE LHOST LPORT [-e] [--whitewash] PATH
                            [--ruby-exec] [--msf-path] PATH [-E] EXPLOIT-FILE-PATH
                            [--rand-agent] [--proxy] PROTO://IP:PORT [-P] AGENT

optional arguments:
  -h, --help            show this help message and exit

search engines:
  possible search engines to use

  -c, --censys          use censys.io as the search engine to gather hosts
  -z, --zoomeye         use zoomeye.org as the search engine to gather hosts
  -s, --shodan          use shodan.io as the search engine to gather hosts
  -a, --all             search all available search engines to gather hosts

requests:
  arguments to edit your requests

  --proxy PROTO://IP:PORT
                        run behind a proxy while performing the searches
  --random-agent        use a random HTTP User-Agent header
  -P USER-AGENT, --personal-agent USER-AGENT
                        pass a personal User-Agent to use for HTTP requests
  -q QUERY, --query QUERY
                        pass your search query

exploits:
  arguments to edit your exploits

  -E PATH, --exploit-file PATH
                        provide a text file to convert into JSON and save for
                        later use
  -C WORKSPACE LHOST LPORT, --config WORKSPACE LHOST LPORT
                        set the configuration for MSF (IE -C default 127.0.0.1
                        8080)
  -e, --exploit         start exploiting the already gathered hosts

misc arguments:
  arguments that don't fit anywhere else

  --ruby-exec           if you need to run the Ruby executable with MSF use
                        this
  --msf-path MSF-PATH   pass the path to your framework if it is not in your
                        ENV PATH
  --whitelist PATH      only exploit hosts listed in the whitelist file

The post AutoSploit: Automated Mass Exploiter appeared first on Tech Chronicles.

It enhances the security assessment process by rapidly scanning and identifying potential vulnerabilities using multi-threaded, ensuring speed and efficiency.

Unlike other scanners, SqliSniper is designed to eliminates false positives through and send alerts upon detection, with the built-in Discord notification functionality.

Key Features

• Time-Based Blind SQL Injection Detection: Pinpoints potential SQL injection vulnerabilities in HTTP headers.
• Multi-Threaded Scanning: Offers faster scanning capabilities through concurrent processing.
• Discord Notifications: Sends alerts via Discord webhook for detected vulnerabilities.
• False Positive Checks: Implements response time analysis to differentiate between true positives and false alarms.
• Custom Payload and Headers Support: Allows users to define custom payloads and headers for targeted scanning.

Installation

git clone https://github.com/danialhalo/SqliSniper.git
cd SqliSniper
chmod +x sqlisniper.py
pip3 install -r requirements.txt

Usage

This will display help for the tool. Here are all the options it supports.

ubuntu:~/sqlisniper$ ./sqlisniper.py -h


███████╗ ██████╗ ██╗     ██╗    ███████╗███╗   ██╗██╗██████╗ ███████╗██████╗
██╔════╝██╔═══██╗██║     ██║    ██╔════╝████╗  ██║██║██╔══██╗██╔════╝██╔══██╗
███████╗██║   ██║██║     ██║    ███████╗██╔██╗ ██║██║██████╔╝█████╗  ██████╔╝
╚════██║██║▄▄ ██║██║     ██║    ╚════██║██║╚██╗██║██║██╔═══╝ ██╔══╝  ██╔══██╗
███████║╚██████╔╝███████╗██║    ███████║██║ ╚████║██║██║     ███████╗██║  ██║
╚══════╝ ╚══▀▀═╝ ╚══════╝╚═╝    ╚══════╝╚═╝  ╚═══╝╚═╝╚═╝     ╚══════╝╚═╝  ╚═╝

                            -: By Muhammad Danial :-

usage: sqlisniper.py [-h] [-u URL] [-r URLS_FILE] [-p] [--proxy PROXY] [--payload PAYLOAD] [--single-payload SINGLE_PAYLOAD] [--discord DISCORD] [--headers HEADERS]
                     [--threads THREADS]

Detect SQL injection by sending malicious queries

options:
  -h, --help            show this help message and exit
  -u URL, --url URL     Single URL for the target
  -r URLS_FILE, --urls_file URLS_FILE
                        File containing a list of URLs
  -p, --pipeline        Read from pipeline
  --proxy PROXY         Proxy for intercepting requests (e.g., http://127.0.0.1:8080)
  --payload PAYLOAD     File containing malicious payloads (default is payloads.txt)
  --single-payload SINGLE_PAYLOAD
                        Single payload for testing
  --discord DISCORD     Discord Webhook URL
  --headers HEADERS     File containing headers (default is headers.txt)
  --threads THREADS     Number of threads

Running SqliSniper

Single Url Scan

The url can be provided with -u flag for single site scan

./sqlisniper.py -u http://example.com

File Input

The -r flag allows SqliSniper to read a file containing multiple URLs for simultaneous scanning.

./sqlisniper.py -r url.txt

Piping URLs

The SqliSniper can also worked with the pipeline input with -p flag

cat url.txt | ./sqlisniper.py -p

The pipeline feature facilitates seamless integration with other tools. For instance, you can utilize tools like subfinder and httpx, and then pipe their output to SqliSniper for mass scanning.

subfinder -silent -d google.com | sort -u | httpx -silent | ./sqlisniper.py -p

Scanning with custom payloads

By default the SqliSniper use the payloads.txt file. However --payload flag can be used for providing custom payloads file.

./sqlisniper.py -u http://example.com --payload mssql_payloads.txt

While using the custom payloads file, ensure that you substitute the sleep time with %__TIME_OUT__%. SqliSniper dynamically adjusts the sleep time iteratively to mitigate potential false positives. The payloads file should look like this.

ubuntu:~/sqlisniper$ cat payloads.txt 
0\"XOR(if(now()=sysdate(),sleep(%__TIME_OUT__%),0))XOR\"Z
"0"XOR(if(now()=sysdate()%2Csleep(%__TIME_OUT__%)%2C0))XOR"Z"
0'XOR(if(now()=sysdate(),sleep(%__TIME_OUT__%),0))XOR'Z

Scanning with Single Payloads

If you want to only test with the single payload --single-payload flag can be used. Make sure to replace the sleep time with %__TIME_OUT__%

./sqlisniper.py -r url.txt --single-payload "0'XOR(if(now()=sysdate(),sleep(%__TIME_OUT__%),0))XOR'Z"

Scanning Custom Header

Headers are saved in the file headers.txt for scanning custom header save the custom HTTP Request Header in headers.txt file.

ubuntu:~/sqlisniper$ cat headers.txt 
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64)
X-Forwarded-For: 127.0.0.1

Sending Discord Alert Notifications

SqliSniper also offers Discord alert notifications, enhancing its functionality by providing real-time alerts through Discord webhooks. This feature proves invaluable during large-scale scans, allowing prompt notifications upon detection.

./sqlisniper.py -r url.txt --discord <web_hookurl>

Multi-Threading

Threads can be defined with --threads flag

 ./sqlisniper.py -r url.txt --threads 10

Note: It is crucial to consider that employing a higher number of threads might lead to potential false positives or overlooking valid issues. Due to the nature of time-based SQL injection it is recommended to use lower thread for more accurate detection.

Usage of this tool for attacking targets without prior mutual consent is strictly prohibited. It is the end user’s responsibility to obey all applicable local, state, and federal laws. Developers assume no liability and are not responsible for any misuse or damage caused by this program.

The post SqliSniper – Enhancing Web Security By Detecting SQL Injection Vulnerabilities With Python appeared first on Tech Chronicles.

This tool is designed for educational purposes, aiming to showcase file vulnerabilities and promote proactive security measures. Rose Grabber emphasizes a didactic approach, highlighting the significance of strengthening data security and using technology ethically.

Rose Grabber: Capture Discord Tokens With Reduced Detection Rates

While this tool has a sophisticated mechanism that could exploit Discord Nitro privileges through compromised accounts, the author strongly urges you not to go down that path for personal gain. Remember, the purpose here is to teach and emphasize the importance of data security and ethical tech use. Stay safe and responsible!

Features:

1. GUI Builder: Create user-friendly interfaces easily.
2. Custom Icon: Set a unique icon for your application.
3. Runs On Startup: Automatically start with Windows.
4. Anti-VM: Detect and evade virtual machine environments.
5. Melt Stub: Make the malware difficult to detect by self-destructing.
6. Fake Error: Display fake error messages to deceive users.
7. File Pumper: Inflate file sizes to hide malicious content.
8. Discord Injection: Inject code into Discord processes.
9. Steals Discord Tokens: Extract user tokens from Discord.
10. Steals Passwords From Many Browsers: Extract stored passwords.
11. Steals Cookies From Many Browsers: Collect browser cookies.
12. Steals History From Many Browsers: Access browsing history.
13. Steals Roblox Cookies: Gather Roblox-related data.
14. Steals IP Information: Collect IP addresses.
15. Steals System Info: Gather system-specific details.
16. Steals Saved Wifi Passwords: Extract saved Wi-Fi passwords.
17. Captures Screenshot: Take screenshots of the user’s desktop.
18. Captures Webcam Image: Access and capture webcam images.
19. Sends All Data Through Discord Webhook: Send stolen data to specified destinations.
20. File Type Selection: Choose between screensaver or executable.
21. Ransomware: Forces the victim to pay a specific amount in Monero or they will lose all their data. Decrypter can be found in components/tools.

Installation:

Click on the provided link to download the repository.

Unzip the downloaded ZIP folder to access its contents.

Since the current install script is not optimized and may not work properly, you need to install the requirements manually. Open a command prompt in the components/scrapedata directory and run this command:

pip uninstall crypto && pip uninstall pycryptodome && pip uninstall pycrypto && pip install -r requiremens.txt.

To run the program, simply double-click the start script (start.bat). This will initiate the builder after successfully installing the requirements.

DOCUMENTATION    DOWNLOAD

The post Rose Grabber: Python-Powered Discord Token Logger appeared first on Tech Chronicles.

HTTPX

Arrow

Python Fire

Starlette

Mypy

FastAPI

Immutables

Expiring Dict

VCR.py

Transformers

Modin

Dash

Detectron 2

Streamlit

Imbalanced-learn

PyTorch

The post 16 Python Libraries to ‘pip’ in 2020 appeared first on Tech Chronicles.

pip install tensorflow keras pickle nltk 

How do Chatbots work?

1. NLU (Natural Language Understanding): The ability of machines to understand human language like English.
2. NLG (Natural Language Generation): The ability of a machine to generate text similar to human written sentences.

Project file structure 

1. Train_chatbot.py – In this file, we will build and train the deep learning model that can classify and identify what the user is asking to the bot.
2. Gui_Chatbot.py – This file is where we will build a graphical user interface to chat with our trained chatbot.
3. Intents.json – The intents file has all the data that we will use to train the model. It contains a collection of tags with their corresponding patterns and responses.
4. Chatbot_model.h5 – This is a hierarchical data format file in which we have stored the weights and the architecture of our trained model.
5. Classes.pkl – The pickle file can be used to store all the tag names to classify when we are predicting the message.
6. Words.pkl – The words.pkl pickle file contains all the unique words that are the vocabulary of our model.

How to build your own chatbot?

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import SGD
import random
import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import json
import pickle
intents_file = open('intents.json').read()
intents = json.loads(intents_file)

words=[]
classes = []
documents = []
ignore_letters = ['!', '?', ',', '.']
for intent in intents['intents']:
    for pattern in intent['patterns']:
        #tokenize each word
        word = nltk.word_tokenize(pattern)
        words.extend(word)
        #add documents in the corpus
        documents.append((word, intent['tag']))
        # add to our classes list
        if intent['tag'] not in classes:
            classes.append(intent['tag'])
print(documents)

# lemmaztize and lower each word and remove duplicates
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_letters]
words = sorted(list(set(words)))
# sort classes
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
print (len(documents), "documents")
# classes = intents
print (len(classes), "classes", classes)
# words = all words, vocabulary
print (len(words), "unique lemmatized words", words)
pickle.dump(words,open('words.pkl','wb'))
pickle.dump(classes,open('classes.pkl','wb'))

# create the training data
training = []
# create empty array for the output
output_empty = [0] * len(classes)
# training set, bag of words for every sentence
for doc in documents:
    # initializing bag of words
    bag = []
    # list of tokenized words for the pattern
    word_patterns = doc[0]
    # lemmatize each word - create base word, in attempt to represent related words
    word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
    # create the bag of words array with 1, if word is found in current pattern
    for word in words:
        bag.append(1) if word in word_patterns else bag.append(0)
    # output is a '0' for each tag and '1' for current tag (for each pattern)
    output_row = list(output_empty)
    output_row[classes.index(doc[1])] = 1
    training.append([bag, output_row])
# shuffle the features and make numpy array
random.shuffle(training)
training = np.array(training)
# create training and testing lists. X - patterns, Y - intents
train_x = list(training[:,0])
train_y = list(training[:,1])
print("Training data is created")

# deep neural networds model
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))
# Compiling model. SGD with Nesterov accelerated gradient gives good results for this model
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
#Training and saving the model 
hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
model.save('chatbot_model.h5', hist)
print("model is created")

import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from keras.models import load_model
model = load_model('chatbot_model.h5')
import json
import random
intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))
def clean_up_sentence(sentence):
    # tokenize the pattern - splitting words into array
    sentence_words = nltk.word_tokenize(sentence)
    # stemming every word - reducing to base form
    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
    return sentence_words
# return bag of words array: 0 or 1 for words that exist in sentence
def bag_of_words(sentence, words, show_details=True):
    # tokenizing patterns
    sentence_words = clean_up_sentence(sentence)
    # bag of words - vocabulary matrix
    bag = [0]*len(words)  
    for s in sentence_words:
        for i,word in enumerate(words):
            if word == s: 
                # assign 1 if current word is in the vocabulary position
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % word)
    return(np.array(bag))
def predict_class(sentence):
    # filter below  threshold predictions
    p = bag_of_words(sentence, words,show_details=False)
    res = model.predict(np.array([p]))[0]
    ERROR_THRESHOLD = 0.25
    results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
    # sorting strength probability
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
    return return_list
def getResponse(ints, intents_json):
    tag = ints[0]['intent']
    list_of_intents = intents_json['intents']
    for i in list_of_intents:
        if(i['tag']== tag):
            result = random.choice(i['responses'])
            break
    return result
#Creating tkinter GUI
import tkinter
from tkinter import *
def send():
    msg = EntryBox.get("1.0",'end-1c').strip()
    EntryBox.delete("0.0",END)
    if msg != '':
        ChatBox.config(state=NORMAL)
        ChatBox.insert(END, "You: " + msg + '\n\n')
        ChatBox.config(foreground="#446665", font=("Verdana", 12 ))
        ints = predict_class(msg)
        res = getResponse(ints, intents)
        ChatBox.insert(END, "Bot: " + res + '\n\n')
        ChatBox.config(state=DISABLED)
        ChatBox.yview(END)
root = Tk()
root.title("Chatbot")
root.geometry("400x500")
root.resizable(width=FALSE, height=FALSE)
#Create Chat window
ChatBox = Text(root, bd=0, bg="white", height="8", width="50", font="Arial",)
ChatBox.config(state=DISABLED)
#Bind scrollbar to Chat window
scrollbar = Scrollbar(root, command=ChatBox.yview, cursor="heart")
ChatBox['yscrollcommand'] = scrollbar.set
#Create Button to send message
SendButton = Button(root, font=("Verdana",12,'bold'), text="Send", width="12", height=5,
                    bd=0, bg="#f9a602", activebackground="#3c9d9b",fg='#000000',
                    command= send )
#Create the box to enter message
EntryBox = Text(root, bd=0, bg="white",width="29", height="5", font="Arial")
#EntryBox.bind("<Return>", send)
#Place all components on the screen
scrollbar.place(x=376,y=6, height=386)
ChatBox.place(x=6,y=6, height=386, width=370)
EntryBox.place(x=128, y=401, height=90, width=265)
SendButton.place(x=6, y=401, height=90)
root.mainloop()

Running the chatbot

python train_chatbot.py

The post How To Build a Chatbot Project Using Python appeared first on Tech Chronicles.

pip install pyhttptest

Single test case definition examples 

To send an HTTP GET Request:

Create a JSON file, for example, data/test_server_status.json

{
  "name": "TEST: Get server status",
  "verb": "GET",
  "endpoint": "/get",
  "host": "https://httpbin.org",
  "headers": {
    "Accept-Language": "en-US"
  }
}

To send an HTTP POST Request:

Create a JSON file, for example, data/test_create_html_bin.json

{
  "name": "TEST: Create an HTML bin",
  "verb": "POST",
  "endpoint": "post",
  "host": "https://httpbin.org",
  "payload": {
    "content": "Hello, world!"
  }
}

• Create a JSON file, for example, data/requests.json

[
  {
    "name": "TEST: List all users",
    "verb": "GET",
    "endpoint": "api/v1/users",
    "host": "http://localhost:8085/",
    "headers": {
      "Accept-Language": "en-US"
    },
    "query_string": {
      "limit": 1
    }
  },
  {
    "name": "TEST: Add a new user",
    "verb": "POST",
    "endpoint": "api/v1/users",
    "host": "http://localhost:8085/",
    "payload": {
      "username": "pyhttptest",
      "email": "admin@pyhttptest.com"
    }
  },
  {
    "name": "TEST: Modify an existing user",
    "verb": "PUT",
    "endpoint": "api/v1/users/XeEsscGqweEttXsgY",
    "host": "http://localhost:8085/",
    "payload": {
      "username": "pyhttptest"
    }
  },
  {
    "name": "TEST: Delete an existing user",
    "verb": "DELETE",
    "endpoint": "api/v1/users/XeEsscGqweEttXsgY",
    "host": "http://localhost:8085/"
  }
]

pyhttptest execute data/test_server_status.json

pyhttptest execute data/requests.json

• name – The name of the test case.
• verb – An HTTP Method.
• endpoint – The resource you want to invoke on the server.
• host – Server host address.
• headers – An HTTP Headers. All HTTP header fields are supported.
• query_string – Query string parameters in the URL after the question mark.
• payload – The data.

tests/

unittest

pytest

live_tests/

json

The post Testing REST APIs easily in Python with pyhttptest appeared first on Tech Chronicles.

Python snippets that can be taken as a reference for your daily work

characters = ['p', 'y', 't', 'h', 'o', 'n']
word = "".join(characters)
print(word) # python

m = [x ** 2 for x in range(5)]
print(m) # [0, 1, 4, 9, 16]

list_a = [1, 2, 3, 4]
list_b = [2, 3, 4, 5]
common_num = [a for a in list_a for b in list_b if a == b]
print(common_num) # [2, 3, 4]

numbers = [30, 42, 28, 50, 15]
for i, num in enumerate(numbers):
    if num % 3 == 0 and num % 5 == 0:
       numbers[i] = 'fizzbuzz'
    elif num % 3 == 0:
       numbers[i] = 'fizz'
    elif num % 5 == 0:
       numbers[i] = 'buzz'
print(numbers) # ['fizzbuzz', 'fizz', 28, 'buzz', 'fizzbuzz']

countries = ['France', 'Germany', 'Canada']
capitals = ['Paris', 'Berlin', 'Ottawa']
for country, capital in zip(countries,capitals):
    print(country, capital) # France Paris 
                              Germany Berlin
                              Canada Ottawa

import itertools
friends = ['Team 1', 'Team 2', 'Team 3', 'Team 4']
list(itertools.combinations(friends, r=2)) # [('Team 1', 'Team 2'),      ('Team 1', 'Team 3'),  ('Team 1', 'Team 4'),  ('Team 2', 'Team 3'),  ('Team 2', 'Team 4'),  ('Team 3', 'Team 4')]

from collections import Countercount = Counter(['a','b','c','d','b','c','d','b'])
print(count) # Counter({'b': 3, 'c': 2, 'd': 2, 'a': 1})

students = ["Peter", "Julia", "Alex"]
marks = [84, 65, 77]
dictionary = dict(zip(students, marks))
print(dictionary) # {'Peter': 84, 'Julia': 65, 'Alex': 77}

t1 = time.clock()
sum([i * i for i in range(1, 100000000)])
t2 = time.clock()
time_diff = t2 - t1
print(f"It took {time_diff} Secs to execute this method") # It took 13.197494000000006 Secs to execute this method

t1 = time.clock()
sum((i * i for i in range(1, 100000000)))
t2 = time.clock()
time_diff = t2 - t1
print(f"It took {time_diff} Secs to execute this method") # It took 9.53867000000001 Secs to execute this method

def multiplication_division(num1, num2):
    return num1*num2, num1/num2product, division = multiplication_division(15, 3)
print("Product=", product, "Quotient =", division) # Product= 45 Quotient = 5.0

sorted([3,5,2,1,4]) # [1, 2, 3, 4, 5]

sorted(['france', 'germany', 'canada', 'india', 'china'], reverse=True) # ['india', 'germany', 'france', 'china', 'canada']

The post 10 Python Tips and Tricks You Should Learn Today appeared first on Tech Chronicles.