9. Reporting
by Dave Mound, Benjamin May, Andrew Mabbitt, Terry Ip, Cameron Buchanan, Mohit, Chr
Python: Penetration Testing for Developers
- Python: Penetration Testing for Developers
- Table of Contents
- Python: Penetration Testing for Developers
- Python: Penetration Testing for Developers
- Credits
- Preface
- 1. Module 1
- 1. Understanding the Penetration Testing Methodology
- 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
- Understanding how systems communicate
- Understanding Nmap
- Nmap libraries for Python
- The Scapy library for Python
- Summary
- 4. Executing Credential Attacks with Python
- 5. Exploiting Services with Python
- 6. Assessing Web Applications with Python
- 7. Cracking the Perimeter with Python
- 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
- 10. Adding Permanency to Python Tools
- 2. Module 2
- 1. Python with Penetration Testing and Networking
- Introducing the scope of pentesting
- Approaches to pentesting
- Introducing Python scripting
- Understanding the tests and tools you'll need
- Learning the common testing platforms with Python
- Network sockets
- Server socket methods
- Client socket methods
- General socket methods
- Moving on to the practical
- Summary
- 2. Scanning Pentesting
- 3. Sniffing and Penetration Testing
- 4. Wireless Pentesting
- 5. Foot Printing of a Web Server and a Web Application
- 6. Client-side and DDoS Attacks
- 7. Pentesting of SQLI and XSS
- 1. Python with Penetration Testing and Networking
- 3. Module 3
- 1. Gathering Open Source Intelligence
- Introduction
- Gathering information using the Shodan API
- Scripting a Google+ API search
- Downloading profile pictures using the Google+ API
- Harvesting additional results from the Google+ API using pagination
- Getting screenshots of websites with QtWebKit
- Screenshots based on a port list
- Spidering websites
- 2. Enumeration
- 3. Vulnerability Identification
- 4. SQL Injection
- 5. Web Header Manipulation
- Introduction
- Testing HTTP methods
- Fingerprinting servers through HTTP headers
- Testing for insecure headers
- Brute forcing login through the Authorization header
- Testing for clickjacking vulnerabilities
- Identifying alternative sites by spoofing user agents
- Testing for insecure cookie flags
- Session fixation through a cookie injection
- 6. Image Analysis and Manipulation
- 7. Encryption and Encoding
- Introduction
- Generating an MD5 hash
- Generating an SHA 1/128/256 hash
- Implementing SHA and MD5 hashes together
- Implementing SHA in a real-world scenario
- Generating a Bcrypt hash
- Cracking an MD5 hash
- Encoding with Base64
- Encoding with ROT13
- Cracking a substitution cipher
- Cracking the Atbash cipher
- Attacking one-time pad reuse
- Predicting a linear congruential generator
- Identifying hashes
- 8. Payloads and Shells
- 9. Reporting
- 1. Gathering Open Source Intelligence
- A. Bibliography
- Index
In this chapter, we will cover the following topics:
- Converting Nmap XML to CSV
- Extracting links from URLs to Maltego
- Extracting e-mails to Maltego
- Parsing Sslscan to CSV
- Generating graphs using
plot.ly
We’ve got recipes throughout this module to perform various aspects of web application testing. So, we’ve got all this information. We’ve got console outputs from our recipes, but how do we collect all this into a useful format? Ideally, we’ll want the output to be in a format that we can use. Or we might want to convert the output from another application such as Nmap, into the format that we’re using. This can either be as comma separated variables (CSV), or possibly a Maltego transform, or any other format that you want to work with.
What’s this Maltego thing you just mentioned? I hear you ask. Maltego is an Open Source Intelligence (OSINT) and forensics application. It has a nice GUI that helps you visualize your information in a nice, pretty, and easy to understand way.
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