Table of Contents for
Python: Penetration Testing for Developers

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Python: Penetration Testing for Developers by Dave Mound Published by Packt Publishing, 2016
  1. Cover
  2. Table of Contents
  3. Python: Penetration Testing for Developers
  4. Python: Penetration Testing for Developers
  5. Python: Penetration Testing for Developers
  6. Credits
  7. Preface
  8. What you need for this learning path
  9. Who this learning path is for
  10. Reader feedback
  11. Customer support
  12. 1. Module 1
  13. 1. Understanding the Penetration Testing Methodology
  14. Understanding what penetration testing is not
  15. Assessment methodologies
  16. The penetration testing execution standard
  17. Penetration testing tools
  18. Summary
  19. 2. The Basics of Python Scripting
  20. Python – the good and the bad
  21. A Python interactive interpreter versus a script
  22. Environmental variables and PATH
  23. Understanding dynamically typed languages
  24. The first Python script
  25. Developing scripts and identifying errors
  26. Python formatting
  27. Python variables
  28. Operators
  29. Compound statements
  30. Functions
  31. The Python style guide
  32. Arguments and options
  33. Your first assessor script
  34. Summary
  35. 3. Identifying Targets with Nmap, Scapy, and Python
  36. Understanding Nmap
  37. Nmap libraries for Python
  38. The Scapy library for Python
  39. Summary
  40. 4. Executing Credential Attacks with Python
  41. Identifying the target
  42. Creating targeted usernames
  43. Testing for users using SMTP VRFY
  44. Summary
  45. 5. Exploiting Services with Python
  46. Understanding the chaining of exploits
  47. Automating the exploit train with Python
  48. Summary
  49. 6. Assessing Web Applications with Python
  50. Identifying hidden files and directories with Python
  51. Credential attacks with Burp Suite
  52. Using twill to walk through the source
  53. Understanding when to use Python for web assessments
  54. Summary
  55. 7. Cracking the Perimeter with Python
  56. Understanding the link between accounts and services
  57. Cracking inboxes with Burp Suite
  58. Identifying the attack path
  59. Gaining access through websites
  60. Summary
  61. 8. Exploit Development with Python, Metasploit, and Immunity
  62. Understanding the Windows memory structure
  63. Understanding memory addresses and endianness
  64. Understanding the manipulation of the stack
  65. Understanding immunity
  66. Understanding basic buffer overflow
  67. Writing a basic buffer overflow exploit
  68. Understanding stack adjustments
  69. Understanding the purpose of local exploits
  70. Understanding other exploit scripts
  71. Reversing Metasploit modules
  72. Understanding protection mechanisms
  73. Summary
  74. 9. Automating Reports and Tasks with Python
  75. Understanding how to create a Python class
  76. Summary
  77. 10. Adding Permanency to Python Tools
  78. Understanding the difference between multithreading and multiprocessing
  79. Building industry-standard tools
  80. Summary
  81. 2. Module 2
  82. 1. Python with Penetration Testing and Networking
  83. Approaches to pentesting
  84. Introducing Python scripting
  85. Understanding the tests and tools you'll need
  86. Learning the common testing platforms with Python
  87. Network sockets
  88. Server socket methods
  89. Client socket methods
  90. General socket methods
  91. Moving on to the practical
  92. Summary
  93. 2. Scanning Pentesting
  94. What are the services running on the target machine?
  95. Summary
  96. 3. Sniffing and Penetration Testing
  97. Implementing a network sniffer using Python
  98. Learning about packet crafting
  99. Introducing ARP spoofing and implementing it using Python
  100. Testing the security system using custom packet crafting and injection
  101. Summary
  102. 4. Wireless Pentesting
  103. Wireless attacks
  104. Summary
  105. 5. Foot Printing of a Web Server and a Web Application
  106. Introducing information gathering
  107. Information gathering of a website from SmartWhois by the parser BeautifulSoup
  108. Banner grabbing of a website
  109. Hardening of a web server
  110. Summary
  111. 6. Client-side and DDoS Attacks
  112. Tampering with the client-side parameter with Python
  113. Effects of parameter tampering on business
  114. Introducing DoS and DDoS
  115. Summary
  116. 7. Pentesting of SQLI and XSS
  117. Types of SQL injections
  118. Understanding the SQL injection attack by a Python script
  119. Learning about Cross-Site scripting
  120. Summary
  121. 3. Module 3
  122. 1. Gathering Open Source Intelligence
  123. Gathering information using the Shodan API
  124. Scripting a Google+ API search
  125. Downloading profile pictures using the Google+ API
  126. Harvesting additional results from the Google+ API using pagination
  127. Getting screenshots of websites with QtWebKit
  128. Screenshots based on a port list
  129. Spidering websites
  130. 2. Enumeration
  131. Performing a ping sweep with Scapy
  132. Scanning with Scapy
  133. Checking username validity
  134. Brute forcing usernames
  135. Enumerating files
  136. Brute forcing passwords
  137. Generating e-mail addresses from names
  138. Finding e-mail addresses from web pages
  139. Finding comments in source code
  140. 3. Vulnerability Identification
  141. Automated URL-based Directory Traversal
  142. Automated URL-based Cross-site scripting
  143. Automated parameter-based Cross-site scripting
  144. Automated fuzzing
  145. jQuery checking
  146. Header-based Cross-site scripting
  147. Shellshock checking
  148. 4. SQL Injection
  149. Checking jitter
  150. Identifying URL-based SQLi
  151. Exploiting Boolean SQLi
  152. Exploiting Blind SQL Injection
  153. Encoding payloads
  154. 5. Web Header Manipulation
  155. Testing HTTP methods
  156. Fingerprinting servers through HTTP headers
  157. Testing for insecure headers
  158. Brute forcing login through the Authorization header
  159. Testing for clickjacking vulnerabilities
  160. Identifying alternative sites by spoofing user agents
  161. Testing for insecure cookie flags
  162. Session fixation through a cookie injection
  163. 6. Image Analysis and Manipulation
  164. Hiding a message using LSB steganography
  165. Extracting messages hidden in LSB
  166. Hiding text in images
  167. Extracting text from images
  168. Enabling command and control using steganography
  169. 7. Encryption and Encoding
  170. Generating an MD5 hash
  171. Generating an SHA 1/128/256 hash
  172. Implementing SHA and MD5 hashes together
  173. Implementing SHA in a real-world scenario
  174. Generating a Bcrypt hash
  175. Cracking an MD5 hash
  176. Encoding with Base64
  177. Encoding with ROT13
  178. Cracking a substitution cipher
  179. Cracking the Atbash cipher
  180. Attacking one-time pad reuse
  181. Predicting a linear congruential generator
  182. Identifying hashes
  183. 8. Payloads and Shells
  184. Extracting data through HTTP requests
  185. Creating an HTTP C2
  186. Creating an FTP C2
  187. Creating an Twitter C2
  188. Creating a simple Netcat shell
  189. 9. Reporting
  190. Converting Nmap XML to CSV
  191. Extracting links from a URL to Maltego
  192. Extracting e-mails to Maltego
  193. Parsing Sslscan into CSV
  194. Generating graphs using plot.ly
  195. A. Bibliography
  196. Index

Chapter 2. The Basics of Python Scripting

Before diving into writing your first Python script, a few concepts should be understood. Learning these items now will help you develop code quicker in the future. This will improve your abilities as a penetration tester or in understanding what an assessor is doing when they are creating real-time custom code and what questions you should be asking. You should also understand how to create the scripts and the goal you are trying to achieve. You will often find out that your scripts will morph over time and the purpose may change. This may happen because you realize that the real need for the script may not be there or that there is an existing tool for the particular capability.

Many scripters find this discouraging, as a project that they may have been working on for a great deal of time you may find that the tool has duplicate features of more advanced tools. Instead of looking at this as a failed project, look at the activity as an experience wherein you learned new concepts and techniques that you did not initially know. Additionally, keep it at the back of your mind at all times when you are developing code snippets that can be used for other projects in the future.

To this end, try and build your code cleanly, comment it with what you are doing, and make it modular so that once you learn how to build functions, they can be cut and pasted into other scripts in the future. The first step in this journey is to describe the computer science glossary at a high level so that you can understand future chapters or other tutorials. Without understanding these basic concepts, you may misunderstand how to achieve your desired results.

Note

Before running any of the scripts in this module, I recommend that you run the setup script on the git repository, which will configure your Kali instance with all the necessary libraries. The script can be found at https://raw.githubusercontent.com/funkandwagnalls/pythonpentest/master/setup.sh.

Understanding the difference between interpreted and compiled languages

Python, like Ruby and Perl, is an interpreted language, which means that the code is turned into a machine language and run as the script is executed. A language that needs to be compiled prior to running, such as Cobol, C, or C++, can be more efficient and faster, as it is compiled prior to execution, but it also means that the code is typically less portable. As compiled code is generated for specific environments, it may not be as useful when you have to move through heterogeneous environments.

Note

A heterogeneous environment is an environment that has multiple system types and different distributions. So, there may be multiple Unix/Linux distributions, Mac OS, and Windows systems.

Interpreted code usually has the benefit of being portable to different locations as long as the interpreter is available. So for Python scripts, as long as the script is not developed for an operating system, the interpreter is installed, and the libraries are natively available, the Python script should work. Always keep in mind that there will be idiosyncrasies in an environment, and before scripts are used, they should be thoroughly tested in similar test beds.

So why should you learn Python over other scripting languages? I am not making this argument here, and the reason is that the best assessors use the tools available in the environment that they are assessing. You will build scripts that are useful for assessing environments, and Python is fantastic for doing this, but when you gain access to a system, it is best to use what is available to you.

Highly secure environments may prevent you from using exploitation frameworks, or the assessment rules may do the same. When this happens, you have to look at what is available on the system to take advantage of and move forward. Today, newer generation Windows systems are compromised with PowerShell. Often in current Mac, Linux, Unix, and Windows Operating System (OS), you can find a version of Python, especially in development environments. On web servers, you will find Ruby, Python, or Perl. On all forms of operating systems, you will find native shell languages. They provide many capabilities, but typically, they have archaic language structures that require more lines of code than other scripting languages to accomplish the same task. Examples of these shell languages would include Bourne-again Shell (BASH), Korn Shell (KSH), Windows Command Shell, and equivalents.

In most exploitation systems, you will find all the languages, as most hacking laptops, or HackTops, use multiple Virtual Machines (VMs) with many operating systems. Older assessment tools were coded in Perl, as the language provided multiple capabilities that other interpreted languages could not provide at that time. Newer tools are typically created in Ruby and Python. In fact, many libraries that are being created today are for improving the capabilities of these languages, specifically for assessing the potential viability an organization has for being compromised by a malicious actor.

Tip

Keep in mind that your HackTop has multiple VMs to provide you with not only attack tools but also a test bed to test your scripts safely. Reverting to a snapshot of a VM on your HackTop is easy, but telling a customer why you damaged their business-critical component with an untested script is not.

Compiled languages are not without value; many tools have been created in C, C++, and Java. Examples of these types of tools include Burp Suite, Cain & Abel, DirBuster, Zed Attack Proxy (ZAP), CSRFtester, and so on. You might notice that most of these tools were generated originally in the early days of assessing environments. As systems have gotten more powerful and interpreters have become more efficient, we have seen additional tools move to languages that are interpreted as against compiled.

So what is the lesson here? Learn as much as you can to operate in as many environments as possible. In this way, when you encounter an obstacle, you can return to the code and script your way to the level of access necessary.