In this recipe, you’ll learn how to harness the power of ChatGPT and the OpenAI API to create comprehensive vulnerability assessment plans using network, system, and business details as input. This recipe is invaluable for both cybersecurity students and beginners looking to familiarize themselves with proper methods and tools for vulnerability assessments, as well as experienced cybersecurity professionals aiming to save time on planning and documentation.

Building upon the skills acquired in Chapter 1, you will delve deeper into establishing the system role of a cybersecurity professional specializing in vulnerability assessments. You’ll learn how to craft effective prompts that generate well-formatted output using Markdown language. This recipe will also expand on the techniques explored in the Enhancing Output with Templates (Application: Threat Report) and Formatting Output as a Table (Application: Security Controls Table...

In this recipe, you will learn how to leverage ChatGPT and the OpenAI API to conduct a threat assessment by providing a threat, attack, or campaign name. By combining the power of ChatGPT with the MITRE ATT&CK framework, you will be able to generate detailed threat reports, tactics, techniques, and procedures (TTPs) mappings, and associated indicators of compromise (IoCs). This information will enable cybersecurity professionals to analyze attack vectors in their environment and extend their capabilities into threat hunting.

Building upon the skills acquired in Chapter 1, this recipe will guide you through establishing the system role of a cybersecurity analyst and engineering effective prompts that generate well-formatted output, including tables. You will learn how to design prompts to obtain the desired output from ChatGPT using both the ChatGPT web UI and a Python script. Additionally, you will learn how...

Vulnerability scanning plays a crucial role in identifying and remediating weaknesses before they can be exploited by malicious actors. The tools we use to conduct these scans, such as NMAP, OpenVAS, or Nessus, offer robust functionality but can often be complex and challenging to navigate, especially for those new to the field or unfamiliar with their advanced options.

This is where our recipe comes into play. It leverages the power of ChatGPT to streamline the process of generating command strings for these tools based on user input. With this recipe, you will be able to create precise command strings that can be directly copied and pasted into a CLI to initiate a vulnerability scan, provided the respective tool is installed.

This recipe is not just about saving time; it’s about enhancing accuracy, understanding, and effectiveness. It is beneficial for those learning vulnerability assessments, those who are new to these tools, and...

As powerful as ChatGPT and the OpenAI API are, they currently have a significant limitation—the token window. This window determines how many characters can be exchanged in a complete message between the user and ChatGPT. Once the token count exceeds this limitation, ChatGPT may lose track of the original context, making the analysis of large bodies of text or documents challenging.

Enter LangChain—a framework designed to navigate around this very hurdle. LangChain allows us to embed and vectorize large groups of text.

Important note

Embedding refers to the process of transforming text into numerical vectors that an ML model can understand and process. Vectorizing, on the other hand, is a technique to encode non-numeric features as numbers. By converting large bodies of text into vectors, we can enable ChatGPT to access and analyze vast amounts of information, effectively turning the text into a knowledgebase...

As powerful as ChatGPT and the OpenAI API are, they currently have a significant limitation—the token window. This window determines how many characters can be exchanged in a complete message between the user and ChatGPT. Once the token count exceeds this limitation, ChatGPT may lose track of the original context, making the analysis of large bodies of text or documents challenging.

Enter LangChain—a framework designed to navigate around this very hurdle. LangChain allows us to embed and vectorize large groups of text.

Important note

Embedding refers to the process of transforming text into numerical vectors that an ML model can understand and process. Vectorizing, on the other hand, is a technique to encode non-numeric features as numbers. By converting large bodies of text into vectors, we can enable ChatGPT to access and analyze vast amounts of information, effectively turning the text into a knowledgebase...