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Home > Security > Penetration Testing > Learning Penetration Testing with Python
Learning Penetration Testing with Python
Learning Penetration Testing with Python

Learning Penetration Testing with Python: Utilize Python scripting to execute effective and efficient penetration tests

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Learning Penetration Testing with Python

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...

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...

Python – the good and the bad

Python is one of the easiest languages for creating a working piece of code that accomplishes tangible results. In fact, Python has a native interactive interpreter through which you can test code directly by just executing the word python at the CLI. This will bring up an interface in which concepts of code can be tested prior to trying to write a script. Additionally, this interface allows a tester to not only test new concepts, but also to import modules or other scripts as modules and use them to create powerful tools.

Not only does this testing capability of Python allow assessors to verify concepts, but they can also avoid dealing with extensive debuggers and test cases to quickly prototype attack code. This is especially important when on an engagement and when determining whether a particular exploit train will net useful results in a timely manner. Most importantly, the use of Python and the importing of specific libraries usually do not break...

A Python interactive interpreter versus a script

There are two ways in which the Python language can be used. One is through an interactive interpreter, that allows quick testing of functions, code snippets, and ideas. The other is through a full-fledged script that can be saved and transported between systems. If you want to try out an interactive interpreter, just type python in your command-line shell.

Note

An interactive interpreter will function the same way in different operating systems, but the libraries and called functions that interact with a system may not. If specific locations are referenced or if commands and/or libraries use operating-system-specific capabilities, the functionality will be different. As such, referencing these details in a script will impact its portability substantially, so it is not considered a leading practice.

Environmental variables and PATH

These variables are important for executing scripts written in Python, not for writing them. If they are not configured, the location of the Python binary has to be referenced by its fully qualified path location. As an example, here is the execution of a Python script without the environmental variable being declared in Windows:

C:\Python27\python wargames_print.py

The following is the equivalent in Linux or Unix if the reference to the proper interpreter is not listed at the top of the script and the file is in your current directory:

/usr/bin/python ./wargames_print.py

In Windows, if the environmental variable is set, you can simply execute the script by typing python and the script name. In Linux and Unix, we add a line at the top of the script to make it more portable. A benefit to us (penetration testers) is that this makes the script useful on many different types of systems, including Windows. This line is ignored by the Windows operating system natively...

Understanding dynamically typed languages

Python is a dynamically typed language, which means many things, but the most crucial aspect is how variables or objects are handled. Dynamically typed languages are usually synonymous with scripting languages, but this is not always the case, just to be clear. What this means to you when you write your script is that variables are interpreted at runtime, so they do not have to defined in size or by content.

The first Python script

Now that you have a basic idea of what Python is, let's create a script. Instead of the famous Hello World! introduction, we are going to use a cult film example. The scripts will define a function, which will print a famous quote from the 1983 cult classic WarGames. There are two ways of doing this, as mentioned previously; the first is through the interactive interpreter, and the second is through a script. Open an interactive interpreter and execute the following line:

print("Shall we play a game?\n")

The preceding print statement will show that the code execution worked. To exit the interactive interpreter, either type exit() or use Ctrl + Z in Windows or Ctrl + D in Linux. Now, create a script in your preferred editing tool, such as vi, vim, emacs, or gedit. Then save the file in /root/Desktop as wargames_print.py:

#!/usr/bin/env python
print("Shall we play a game?\n")

After saving the file, run it with the following command:

python /root...

The first Python script

Now that you have a basic idea of what Python is, let's create a script. Instead of the famous Hello World! introduction, we are going to use a cult film example. The scripts will define a function, which will print a famous quote from the 1983 cult classic WarGames. There are two ways of doing this, as mentioned previously; the first is through the interactive interpreter, and the second is through a script. Open an interactive interpreter and execute the following line:

print("Shall we play a game?\n")

The preceding print statement will show that the code execution worked. To exit the interactive interpreter, either type exit() or use Ctrl + Z in Windows or Ctrl + D in Linux. Now, create a script in your preferred editing tool, such as vi, vim, emacs, or gedit. Then save the file in /root/Desktop as wargames_print.py:

#!/usr/bin/env python
print("Shall we play a game?\n")

After saving the file, run it with the following command:

python /root/Desktop/wargames_print...

Developing scripts and identifying errors

Before we jump into creating large-scale scripts, you need to understand the errors that can be produced. If you start creating scripts and generating a bunch of errors, you may get discouraged. Keep in mind that Python does a pretty good job at directing you to what you need to look at. Often, however, the producer of the error is either right before the line referenced or the function called. This in turn can be misleading, so to prevent discouragement, you should understand the definitions that Python may reference in the errors.

Reserved words, keywords, and built-in functions

Reserved words, keywords, and built-in functions are also known as prohibited, which means that the name cannot be used as a variable or function. If the word or function is reused, an error will be shown. There are set words and built-in functions natively within Python, and depending on the version you are using, they can change. You should not worry too much about this...

Python formatting

This language's greatest selling feature for me is its formatting. It takes very little work to put a script together, and because of its simplistic formatting requirements, you reduce chances of errors. For experienced programmers, the loathsome ; and {} signs will no longer impact your development time due to syntax errors.

Indentation

The most important thing to remember in Python is indentation. Python uses indents to show where logic blocks are changed. So, if you are writing a simple print script as mentioned earlier, you are not necessarily going to see this, but if you are writing an if statement, you will. See the following example, which prints the statement previously mentioned here:

#!/usr/bin/env python
execute=True
if execute != False:
    print("Do you want to play a game?\n")

More details on how this script operates and executes can be found in the Compound statements section of this chapter. The following example prints the statement to the screen if execute...

Python variables

The Python scripting language has five types of variables: numbers, strings, lists, dictionaries, and tuples. These variables have different intended purposes, reasons for use, and methods of declaration. Before seeing how these variable types work, you need to understand how to debug your variables and ensure that your scripts are working.

Note

Lists, tuples, and dictionaries fall under a variable category know as data structures. This chapter covers enough details to get you off the ground and running, but most of the questions you notice about Python in help forums are related to proper use and handling of data structures. Keep this in mind when you start venturing on your own projects outside of the details given in this book. Additional information about data structures and how to use them can be found at https://docs.python.org/2/tutorial/datastructures.html.

Debugging variable values

The simple solution for debugging variable values is to make sure that the expected data...

Operators

Operators in Python are symbols that represent functional executions.

Note

More details about this can be found at https://docs.python.org/2/library/operator.html.

The important thing to remember is that Python has extensive capabilities that allow complex mathematical and comparative operations. Only a few of them will be covered here to prepare you for more detailed work.

Comparison operators

A comparison operator checks whether a condition is true or false based on the method of evaluation. In simpler terms, we try to determine whether one value equals, does not equal, is greater than, is less than, is greater than or equal to, or is less than or equal to another value. Interestingly enough, the Python comparison operators are very straightforward.

The following table will help define the details of operators:

Comparison test

Operator

Are the two values equal?

==

Are the values not equal?

!=

Is the value on the left greater than the value on the right?

>

Is the value on...

Compound statements

Compound statements contain other statements. This means a test or execution while true or false executes the statements within itself. The trick is to write statements so that they are efficient and effective. Examples of this include if then statements, loops, and exception handling.

The if statements

An if statement tests for a specific condition, and if that condition is met (or not met), then the statement is executed. The if statement can include a simple check to see whether a variable is true or false, and then print the details, as shown in the following example:

x = 1
if x == 1:
    print("The variable x has a value of 1")

The if statement can even be used to check for multiple conditions at the same time. Keep in mind that it will execute the first portion of the compound statement that meets the condition and skip the rest. Here is an example that builds on the previous one, using else and elif statements. The else statement is a catch all if none of the if...

Functions

Python functions allow a scripter to create a repeatable task and have it called frequently throughout the script. When a function is part of a class or module, it means that a certain portion of the script can be called specifically from another script, also known as a module, once imported to execute a task. An additional benefit in using Python functions is the reduction of script size. An often unexpected benefit is the ability to copy functions from one script to another, speeding up development.

The impact of dynamically typed languages on functions on functions

Remember that variables hold references to objects, so as the script is written, you are executing tests with variables that reference the value. One fact about this is that the variable can change and can still point to the original value. When a variable is passed to a function through a parameter, it is done as an alias of the original object. So, when you are writing a function, the variable name within the function...

The Python style guide

When writing your scripts, there are a few naming conventions to observe that are common to scripting and programming. These conventions are more of guidelines and best practices than hard rules, which means that you will hear opinions on both sides. As scripting is a form of art, you will see examples that rebut these suggestions, but following them will improve readability.

Note

Most of the suggestions here were borrowed from the style guide for Python, which can be found at http://legacy.python.org/dev/peps/pep-0008/, and follow-on style guides.

If you see specifics here that do not directly match this guide, keep in mind that all assessors develop habits and styles that differ. The trick is to incorporate as many of the best practices as possible while not impacting the speed and quality of development.

Classes

Classes typically begin with an uppercase letter, and the rest of the first word is lowercase. Each word after that starts with an uppercase letter as well....

Arguments and options

There are multiple ways in which arguments can be passed to scripts; we will cover more on this in future chapters, as they are applicable to specific scripts. The simplest way to take arguments is to pass them without options. Arguments are the values passed to scripts to give them some dynamic capability.

Options are flags that represent specific calls to the script, stating the arguments that are going to be provided. In other words, if you want to get the help or usage instructions for a script, you typically pass the -h option. If you write a script that accepts both IP addresses and MAC addresses, you could configure it to use different options to signify the data that is about to be presented to it.

Writing scripts to take options is significantly more detailed, but it is not as hard as people make it out to be. For now, let's just look at basic argument passing. Arguments can be made natively with the sys library and the argv function. When arguments are passed...

Your first assessor script

Now that you have understood the basics of creating scripts in Python, let's create a script that will actually be useful to you. In later chapters, you will need to know your local and public IP addresses for each interface, hostname, Media Access Control (MAC) addresses, and Fully Qualified Domain Name (FQDN). The script that follows here demonstrates how to execute all of these. A few of the concepts here may still seem foreign, especially how IP and MAC addresses are extracted from interfaces. Do not worry about that; this is not the script you are going to write. You can use this script if you like, but it is here to show you that you can salvage components of scripts—even seemingly complex ones—to develop your own simple scripts.

Note

This script uses a technique to extract IP addresses for Linux/Unix systems by querying the details based on an interface that has been used in several Python modules and examples. The specific recipe for this technique can be...

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Description

Utilize Python scripting to execute effective and efficient penetration tests About This Book Understand how and where Python scripts meet the need for penetration testing Familiarise yourself with the process of highlighting a specific methodology to exploit an environment to fetch critical data Develop your Python and penetration testing skills with real-world examples Who This Book Is For If you are a security professional or researcher, with knowledge of different operating systems and a conceptual idea of penetration testing, and you would like to grow your knowledge in Python, then this book is ideal for you. What You Will Learn Familiarise yourself with the generation of Metasploit resource files Use the Metasploit Remote Procedure Call (MSFRPC) to automate exploit generation and execution Use Python’s Scapy, network, socket, office, Nmap libraries, and custom modules Parse Microsoft Office spreadsheets and eXtensible Markup Language (XML) data files Write buffer overflows and reverse Metasploit modules to expand capabilities Exploit Remote File Inclusion (RFI) to gain administrative access to systems with Python and other scripting languages Crack an organization’s Internet perimeter Chain exploits to gain deeper access to an organization’s resources Interact with web services with Python In Detail Python is a powerful new-age scripting platform that allows you to build exploits, evaluate services, automate, and link solutions with ease. Python is a multi-paradigm programming language well suited to both object-oriented application development as well as functional design patterns. Because of the power and flexibility offered by it, Python has become one of the most popular languages used for penetration testing. This book highlights how you can evaluate an organization methodically and realistically. Specific tradecraft and techniques are covered that show you exactly when and where industry tools can and should be used and when Python fits a need that proprietary and open source solutions do not. Initial methodology, and Python fundamentals are established and then built on. Specific examples are created with vulnerable system images, which are available to the community to test scripts, techniques, and exploits. This book walks you through real-world penetration testing challenges and how Python can help. From start to finish, the book takes you through how to create Python scripts that meet relative needs that can be adapted to particular situations. As chapters progress, the script examples explain new concepts to enhance your foundational knowledge, culminating with you being able to build multi-threaded security tools, link security tools together, automate reports, create custom exploits, and expand Metasploit modules. Style and approach This book is a practical guide that will help you become better penetration testers and/or Python security tool developers. Each chapter builds on concepts and tradecraft using detailed examples in test environments that you can simulate.

Who is this book for?

If you are a security professional or researcher, with knowledge of different operating systems and a conceptual idea of penetration testing, and you would like to grow your knowledge in Python, then this book is ideal for you.

What you will learn

  • Familiarise yourself with the generation of Metasploit resource files
  • Use the Metasploit Remote Procedure Call (MSFRPC) to automate exploit generation and execution
  • Use Python's Scapy, network, socket, office, Nmap libraries, and custom modules
  • Parse Microsoft Office spreadsheets and eXtensible Markup Language (XML) data files
  • Write buffer overflows and reverse Metasploit modules to expand capabilities
  • Exploit Remote File Inclusion (RFI) to gain administrative access to systems with Python and other scripting languages
  • Crack an organization's Internet perimeter
  • Chain exploits to gain deeper access to an organization's resources
  • Interact with web services with Python
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Length: 314 pages
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Language : English
ISBN-13 : 9781785282324
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Languages :
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Concepts :
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Category :
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Languages :
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Concepts :
Penetration Testing

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Table of Contents

11 Chapters
1. Understanding the Penetration Testing Methodology Chevron down icon Chevron up icon
1. Understanding the Penetration Testing Methodology
An overview of penetration testing
Understanding what penetration testing is not
Assessment methodologies
The penetration testing execution standard
Penetration testing tools
Summary
2. The Basics of Python Scripting Chevron down icon Chevron up icon
2. The Basics of Python Scripting
Understanding the difference between interpreted and compiled languages
Python – the good and the bad
A Python interactive interpreter versus a script
Environmental variables and PATH
Understanding dynamically typed languages
The first Python script
Developing scripts and identifying errors
Python formatting
Python variables
Operators
Compound statements
Functions
The Python style guide
Arguments and options
Your first assessor script
Summary
3. Identifying Targets with Nmap, Scapy, and Python Chevron down icon Chevron up icon
3. Identifying Targets with Nmap, Scapy, and Python
Understanding how systems communicate
Understanding Nmap
Nmap libraries for Python
The Scapy library for Python
Summary
4. Executing Credential Attacks with Python Chevron down icon Chevron up icon
4. Executing Credential Attacks with Python
The types of credential attacks
Identifying the target
Creating targeted usernames
Testing for users using SMTP VRFY
Summary
5. Exploiting Services with Python Chevron down icon Chevron up icon
5. Exploiting Services with Python
Understanding the new age of service exploitation
Understanding the chaining of exploits
Automating the exploit train with Python
Summary
6. Assessing Web Applications with Python Chevron down icon Chevron up icon
6. Assessing Web Applications with Python
Identifying live applications versus open ports
Identifying hidden files and directories with Python
Credential attacks with Burp Suite
Using twill to walk through the source
Understanding when to use Python for web assessments
Summary
7. Cracking the Perimeter with Python Chevron down icon Chevron up icon
7. Cracking the Perimeter with Python
Understanding today's perimeter
Understanding the link between accounts and services
Cracking inboxes with Burp Suite
Identifying the attack path
Gaining access through websites
Summary
8. Exploit Development with Python, Metasploit, and Immunity Chevron down icon Chevron up icon
8. Exploit Development with Python, Metasploit, and Immunity
Getting started with registers
Understanding the Windows memory structure
Understanding memory addresses and endianness
Understanding the manipulation of the stack
Understanding immunity
Understanding basic buffer overflow
Writing a basic buffer overflow exploit
Understanding stack adjustments
Understanding the purpose of local exploits
Understanding other exploit scripts
Reversing Metasploit modules
Understanding protection mechanisms
Summary
9. Automating Reports and Tasks with Python Chevron down icon Chevron up icon
9. Automating Reports and Tasks with Python
Understanding how to parse XML files for reports
Understanding how to create a Python class
Summary
10. Adding Permanency to Python Tools Chevron down icon Chevron up icon
10. Adding Permanency to Python Tools
Understanding logging within Python
Understanding the difference between multithreading and multiprocessing
Building industry-standard tools
Summary
Index Chevron down icon Chevron up icon
Index

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(7 Ratings)
5 star 85.7%
4 star 0%
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2 star 0%
1 star 14.3%
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Andrew Feb 21, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This textbook served as a detailed technical overview and guide for conducting network and system attacks using tools that you will build. You are first introduced to established assessment methodologies, to include the Open Source Security Testing Methodology Manual (OSSTMM), The Open Web Application Security Project (OWASP), and the Penetration Testing Execution Standard (PTES). The PTES is the primary methodology referenced in the book.Several well-known and established penetration testing tools are covered, to include Metasploit, NMAP, Veil, and John the Ripper. An introductory chapter on Python coding is included for readers who have not used Python extensively before. The book covers nearly every phase in many cyber-attack methodologies, to include initial recon, exploitation, brute force attacks, lateral movement, and exploit development, all of which are covered from a Python developer's point-of-view. The author provided enough preliminary review information in each chapter to ensure the reader understands what is occurring and what is being affected with penetration tools they are developing.
Amazon Verified review Amazon
Choolwe Nalubamba Mar 14, 2016
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Excellent book dealing with both concepts and their respective practical implementation.
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Timoteo Jan 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great start to the subject. The author put in a good amount of time with some background/explanatory information. So, that was appreciated. I would, however, have liked the code to be written in Python 3.X, versus 2.X, since that's the direction the language is progressing towards.
Amazon Verified review Amazon
Matt Mar 17, 2016
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The best part about this book is that it isn't like the hundreds of others that make assumptions about prior knowledge. I was able to take it, understand its concepts, and apply them without having to look too far beyond. Some knowledge of penetration testing methodology and basic programming concepts apply, but this book doesn't require you to have taken advanced computer science sources prior to picking it up, understanding it, and applying it immediately. Very well done.
Amazon Verified review Amazon
Nathan Apr 30, 2016
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Learning Penetration Testing with Python by Christopher Duffy is a must have. This book takes the reader through the fundamentals of penetration testing in an easy-to-read fashion while demonstrating the functionality of Python and its uses in penetration testing. Unlike other hacking with Python books, this work does not assume that the reader is familiar with Python. This work does an excellent job of explaining key Python concepts at an introductory level. Likewise, as the reader progresses through the book, the reader is able to understand and implement more complex Python and penetration techniques. Although the primary focus of this book is the use of Python, it would not be complete without the use of other commonly used tools. The incorporation of Nmap, Metasploit, and other tools allows the reader to become familiar with those tools and incorporate them in Python scripting. This book provides a sound foundation for the reader to be able to develop his or her own tools.Anybody looking to learn Python or to learn more about penetration testing should buy this book. It is a good read and is definitely educational.
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About the author

Profile icon Christopher Duffy
Christopher Duffy
Christopher Duffy currently leads cybersecurity and penetration testing engagements globally. He has a specialization in advanced technical testing, including penetration testing and security assessment done to evaluate an organization's security strategy from a malicious actor's perspective. He has worked a lot with both network and system engineering teams to evaluate critical system data flows, and identified areas where controls can be put in place to prevent a breach of sensitive or critical data. His work with multiple organizations has been key to protecting resources based on the information they have held, which has helped reduce risks while maintaining resilient and cost-effective security postures. Chris has over 12 years of experience in the information technology and security areas, including security consultation, with a focus on business risk. He has helped build advanced attack and penetration teams. The work that his teams have done has encompassed everything from threat modeling and penetration tests to firewall reviews and FedRAMP readiness assessments. Chris has led, managed, and executed over 400 engagements for Fortune 500 companies, U.S. government entities, medical providers and payers, educational institutes, financial services, research organizations, and cloud providers. For almost a decade prior to private sector work, Chris was a cyber warfare specialist, senior systems engineer, and network infrastructure supervisor for the United States Air Force (USAF). He has been honored with numerous technical and leadership awards. Some of these include the (ISC)2 Information Security Leadership Award (ISLA) for the information security practitioner category in 2013, the noncommissioned officer of the year (both at the base and wing levels) in 2011, and the top technician within the cyber transport career field for the United States Air Force (USAF) Intelligence Surveillance and Reconnaissance Agency. He is a distinguished graduate of USAF network warfare training and has publications to his credit in SANS Reading Room, Hackin9 magazine, eForensics magazine and PenTest magazine. He holds 23 certifications, a degree in computer science, and a master's degree in information security and assurance.
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