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Are you ready to unleash the potential of AI-driven cybersecurity? This cookbook takes you on a journey toward enhancing your cybersecurity skills, whether you’re a novice or a seasoned professional. By leveraging cutting-edge generative AI and large language models such as ChatGPT, you'll gain a competitive advantage in the ever-evolving cybersecurity landscape.
ChatGPT for Cybersecurity Cookbook shows you how to automate and optimize various cybersecurity tasks, including penetration testing, vulnerability assessments, risk assessment, and threat detection. Each recipe demonstrates step by step how to utilize ChatGPT and the OpenAI API to generate complex commands, write code, and even create complete tools. You’ll discover how AI-powered cybersecurity can revolutionize your approach to security, providing you with new strategies and techniques for tackling challenges. As you progress, you’ll dive into detailed recipes covering attack vector automation, vulnerability scanning, GPT-assisted code analysis, and more. By learning to harness the power of generative AI, you'll not only expand your skillset but also increase your efficiency.
By the end of this cybersecurity book, you’ll have the confidence and knowledge you need to stay ahead of the curve, mastering the latest generative AI tools and techniques in cybersecurity.
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ChatGPT for Cybersecurity Cookbook
Learn practical generative AI recipes to supercharge your cybersecurity skills
Clint Bodungen
ChatGPT for Cybersecurity Cookbook
Copyright © 2024 Packt Publishing
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To my wife, Ashley, for her unwavering support throughout the many weekends and hours burning the midnight oil for this project. To my sons, Caleb and Connor, the future is what you make of it. You can accomplish anything if you just believe.
– Clint Bodungen
Foreword
In the relentless cyber battleground, where threats morph with each tick of the clock, generative artificial intelligence (AI) emerges as our digital sentinel. ChatGPT and its kin are not mere tools; they are force multipliers in our cyber arsenals. We’re talking about a paradigm shift here – generative AI doesn’t just uplift; it transforms the cybersecurity landscape. It lets us run rings around potential threats, streamline security measures, and forecast nefarious plots with an astuteness that’s simply otherworldly.
This isn’t just tech talk; it’s about real muscle in the fight against digital adversaries. Imagine crafting a cyber training regimen so robust that it catapults neophytes into seasoned defenders within the data trenches. Generative AI is that game-changer, shattering the barriers to entry, democratizing the field, and nurturing a new generation of cyber mavens.
But there’s more. With generative AI, we dive into data oceans and surface with those elusive security insights – the kind that traditional tools would miss. This is about harnessing AI to not just respond to threats but also to anticipate them, to be steps ahead of the adversary. We’re entering an era where our collaboration with AI amplifies our strategic nous, sharpens our foresight, and fortifies our resilience.
As we join forces with AI, we’re not just bolstering defenses; we’re fostering a culture of cybersecurity innovation. We’re empowering minds to push beyond the conventional, to envision a digital realm where safety is the norm, not the exception. This book is a testament to that vision, a guide on wielding AI’s might to safeguard our cyber frontiers. Welcome to the future – a future where we stand united with AI in the vanguard of cybersecurity.
Aaron Crow
OT Cybersecurity Professional & Thought Leader
Host of PrOTect IT All Podcast
Contributors
About the author
Clint Bodungen is a globally recognized cybersecurity professional and thought leader with 25+ years of experience, and author of Hacking Exposed: Industrial Control Systems. He is a U.S. Air Force veteran, has worked for notable cybersecurity firms Symantec, Booz Allen Hamilton, and Kaspersky Lab, and is a co-founder of ThreatGEN, a cybersecurity gamification and training firm. Clint has been at the forefront of integrating gamification and AI into cybersecurity with his flagship product, ThreatGEN® Red vs. Blue, the world’s first online multiplayer computer game designed to teach real-world cybersecurity. Clint continues his pursuit to help revolutionize the cybersecurity industry using gamification and generative AI.
I would first like to thank my amazing team at Packt Publishing for their patience and their trust in me to write this book. And special thanks to the cybersecurity community and the pioneers of the AI industry.
About the reviewers
Aaron Shbeeb is a lifelong programmer, cybersecurity enthusiast, and game developer. He has programmed in over a dozen programming languages both personally and professionally. He has also worked as a penetration tester and vulnerability researcher. Lately, his passion has been for developing ThreatGEN® Red vs. Blue, a cybersecurity training video game that he co-founded/co-developed with Clint Bodungen. Developing that game allows him to practice some of his favorite parts of software development such as system design, machine learning, and AI.
Pascal Ackerman, a principal security consultant, began his career in IT in 1999. He is a seasoned industrial security professional with a degree in electrical engineering and experience in industrial network design and support, information and network security, risk assessments, penetration testing, threat hunting, and forensics. His passion lies in analyzing new and existing threats to Industrial Control System (ICS) environments and he fights cyber adversaries both from his home base and while traveling the world with his family as a digital nomad.
Bradley Jackson navigates the intricate world of cybersecurity with a quiet dedication to Python and emerging technologies. His journey, though marked by meaningful professional accomplishments, finds its truest joy in life’s simpler facets. At heart, Bradley is a family man, deeply devoted to his wife Kayla and their four children. This grounding influence of family life in Arkansas beautifully complements his thoughtful contributions to the ChatGPT for Cybersecurity Cookbook, reflecting a blend of practical wisdom with a down-to-earth approach to technology.
Table of Contents
Prefacexv
1
Getting Started: ChatGPT, the OpenAI API, and Prompt Engineering1
Getting the most out of this book – get to know your free benefits 3
Technical requirements5
Setting up a ChatGPT Account5
Getting ready5
How to do it…5
How it works…7
There’s more…7
Creating an API Key and interacting with OpenAI8
Getting ready8
How to do it…8
How it works…9
There’s more…10
Basic Prompting (Application: Finding Your IP Address)13
Getting ready13
How to do it…14
How it works…17
There’s more…17
Applying ChatGPT Roles (Application : AI CISO)18
Getting ready18
How to do it…18
How it works…20
There’s more…20
Enhancing Output with Templates (Application: Threat Report)21
Getting ready21
How to do it…21
How it works…23
There’s more…23
Formatting Output as a Table (Application: Security Controls Table)24
Getting ready24
How to do it…24
How it works…26
There’s more…26
Setting the OpenAI API Key as an Environment Variable26
Getting ready26
How to do it…26
How it works…28
There’s more…28
Sending API Requests and Handling Responses with Python28
Getting ready28
How to do it…28
How it works…30
There’s more…31
Using Files for Prompts and API Key Access32
Getting ready32
How to do it…32
How it works...34
There’s more...34
Using Prompt Variables (Application: Manual Page Generator)34
Getting ready34
How to do it…35
How it works…37
There’s more...38
2
Vulnerability Assessment39
Technical requirements40
Creating Vulnerability Assessment Plans40
Getting ready40
How to do it…41
How it works…45
There’s more…45
Threat Assessment using ChatGPT and the MITRE ATT&CK framework55
Getting ready55
How to do it…56
How it works…60
There’s more…61
GPT-Assisted Vulnerability Scanning67
Getting ready67
How to do it…68
How it works…70
There’s more…70
Analyzing Vulnerability Assessment Reports using LangChain71
Getting ready72
How to do it…72
How it works…76
There’s more…77
3
Code Analysis and Secure Development79
Technical requirements80
Secure Software Development Lifecycle (SSDLC) Planning (Planning Phase)80
Getting ready81
How to do it…81
How it works…82
There’s more…83
Security Requirement Generation (Requirements Phase)84
Getting ready84
How to do it…84
How it works…86
There’s more…86
Generating Secure Coding Guidelines (Design Phase)87
Getting ready87
How to do it…88
How it works…89
There’s more…90
Analyzing Code for Security Flaws and Generating Custom Security Testing Scripts (Testing Phase)90
Getting ready91
How to do it…92
How it works…93
There’s more…94
Generating Code Comments and Documentation (Deployment/Maintenance Phase)98
Getting ready99
How to do it…99
How it works…102
There’s more…103
4
Governance, Risk, and Compliance (GRC)109
Technical requirements110
Security Policy and Procedure Generation110
Getting ready111
How to do it…111
How it works…112
There’s more…113
ChatGPT-Assisted Cybersecurity Standards Compliance120
Getting ready120
How to do it…120
How it works…122
There’s more…123
Creating a Risk Assessment Process123
Getting ready124
How to do it…124
How it works…132
There’s more…133
ChatGPT-Assisted Risk Ranking and Prioritization134
Getting ready134
How to do it…134
How it works…138
There’s more…139
Building Risk Assessment Reports139
Getting ready139
How to do it…140
How it works…147
There’s more…148
5
Security Awareness and Training149
Technical requirement150
Developing Security Awareness Training Content150
Getting ready151
How to do it…151
How it works…160
There’s more…161
Assessing Cybersecurity Awareness161
Getting ready162
How to do it…162
How it works…164
There’s more…165
Interactive Email Phishing Training with ChatGPT170
Getting ready171
How to do it…171
How it works…172
There’s more…174
ChatGPT-Guided Cybersecurity Certification Study177
Getting ready177
How to do it…178
How it works…178
There’s more…179
Gamifying Cybersecurity Training181
Getting ready182
How to do it…182
How it works…184
There’s more…185
6
Red Teaming and Penetration Testing187
Technical requirements188
Creating red team scenarios using MITRE ATT&CK and the OpenAI API188
Getting ready189
How to do it…189
How it works…196
There’s more…197
Social media and public data OSINT with ChatGPT198
Getting ready198
How to do it…198
How it works…200
There’s more…201
Google Dork automation with ChatGPT and Python201
Getting ready202
How to do it…202
How it works…207
There’s more…207
Analyzing job postings OSINT with ChatGPT208
Getting ready209
How to do it…209
How it works…213
There’s more…214
GPT-powered Kali Linux terminals215
Getting ready215
How to do it…216
How it works…220
There’s more…221
7
Threat Monitoring and Detection223
Technical requirements224
Threat Intelligence Analysis225
Getting ready225
How to do it…225
How it works…226
There’s more…227
Real-Time Log Analysis231
Getting ready231
How to do it…232
How it works…237
There’s more…238
Detecting APTs using ChatGPT for Windows Systems238
Getting ready239
How to do it…239
How it works…243
There’s more…244
Building Custom Threat Detection Rules245
Getting ready245
How to do it…245
How it works…247
There’s more…248
Network Traffic Analysis and Anomaly Detection with PCAP Analyzer248
Getting ready248
How to do it…249
How it works…253
There’s more…254
8
Incident Response255
Technical requirements256
ChatGPT-assisted incident analysis and triage256
Getting ready256
How to do it…257
How it works…257
There’s more…258
Generating incident response playbooks259
Getting ready259
How to do it…259
How it works…260
There’s more…260
ChatGPT-assisted root cause analysis266
Getting ready266
How to do it…267
How it works…268
There’s more…269
Notes of caution269
Automated briefing reports and incident timeline reconstruction270
Getting ready270
How to do it…271
How it works…276
There’s more…277
Notes of caution278
9
Using Local Models and Other Frameworks279
Technical requirements280
Implementing local AI models for cybersecurity analysis with LMStudio280
Getting ready280
How to do it…281
How it works…287
There’s more…287
Local threat hunting with Open Interpreter288
Getting ready288
How to do it…288
How it works…290
There’s more…291
Enhancing penetration testing with Shell GPT292
Getting ready293
How to do it…293
How it works…295
There’s more…296
Reviewing IR Plans with PrivateGPT297
Getting ready297
How to do it…297
There’s more…300
Fine-tuning LLMs for cybersecurity with Hugging Face’s AutoTrain301
Getting ready301
How to do it…301
How it works…305
There’s more…306
10
The Latest OpenAI Features307
Technical requirements308
Analyzing network diagrams with OpenAI’s Image Viewer309
Getting ready309
How to do it…309
How it works…311
There’s more…311
Creating Custom GPTs for Cybersecurity Applications312
Getting ready312
How to do it…313
How it works…324
There’s more…325
Monitoring Cyber Threat Intelligence with Web Browsing 326
Getting ready326
How to do it…326
How it works…328
There’s more…329
Vulnerability Data Analysis and Visualization with ChatGPT Advanced Data Analysis 329
Getting ready330
How to do it…330
How it works…330
There’s more…330
Building Advanced Cybersecurity Assistants with OpenAI 331
Getting ready331
How to do it…332
How it works…336
There’s more…337
11
Unlock Your Book’s Exclusive Benefits341
Index343
Other Books You May Enjoy350
1
Getting Started: ChatGPT, the OpenAI API, and Prompt Engineering
ChatGPT is a large language model (LLM) developed by OpenAI, which is specifically designed to generate context-aware responses and content based on the prompts provided by users. It leverages the power of generative AI to understand and respond intelligently to a wide range of queries, making it a valuable tool for numerous applications, including cybersecurity.
Important note
Generative AI is a branch of artificial intelligence (AI) that uses machine learning (ML) algorithms and natural language processing (NLP) to analyze patterns and structures within a dataset and generate new data that resembles the original dataset. You likely use this technology every day if you use autocorrect in word processing applications, mobile chat apps, and more. That said, the advent of LLMs goes far beyond simple autocomplete.
LLMs are a type of generative AI that are trained on massive amounts of text data, enabling them to understand context, generate human-like responses, and create content based on user input. You may have already used LLMs if you have ever communicated with a helpdesk chatbot.
GPT stands for Generative Pre-Trained Transformer and, as the name suggests, is an LLM that has been pre-trained to improve accuracy and/or provide specific knowledge-based data generation.
ChatGPT has raised concerns about plagiarism in some academic and content-creation communities. It has also been implicated in misinformation and social engineering campaigns due to its ability to generate realistic and human-like text. However, its potential to revolutionize various industries cannot be ignored. In particular, LLMs have shown great promise in more technical fields, such as programming and cybersecurity, due to their deep knowledge base and ability to perform complex tasks such as instantly analyzing data and even writing fully functional code.
In this chapter, we will guide you through the process of setting up an account with OpenAI, familiarizing yourself with ChatGPT, and mastering the art of prompt engineering (the key to leveraging the real power of this technology). We will also introduce you to the OpenAI API, equipping you with the necessary tools and techniques to harness ChatGPT’s full potential.
You’ll begin by learning how to create a ChatGPT account and generate an API key, which serves as your unique access point to the OpenAI platform. We’ll then explore basic ChatGPT prompting techniques using various cybersecurity applications, such as instructing ChatGPT to write Python code that finds your IP address and simulating an AI CISO role by applying ChatGPT roles.
We’ll dive deeper into enhancing your ChatGPT outputs with templates to generate comprehensive threat reports, as well as formatting output as tables for improved presentation, such as creating a security controls table. As you progress through this chapter, you’ll learn how to set the OpenAI API key as an environment variable to streamline your development process, send requests and handle responses with Python, efficiently use files for prompts and API key access, and effectively employ prompt variables to create versatile applications, such as generating manual pages based on user inputs. By the end of this chapter, you’ll have a solid understanding of the various aspects of ChatGPT and how to utilize its capabilities in the cybersecurity domain.
Tip
Even if you are already familiar with the basic ChatGPT and OpenAI API setup and mechanics, it will still be advantageous for you to review the recipes in Chapter 1 as they are almost all set within the context of cybersecurity, which is reflected through some of the prompting examples.
In this chapter, we will cover the following recipes:
Setting up a ChatGPT AccountCreating an API Key and interacting with OpenAIBasic prompting (Application: Finding Your IP Address)Applying ChatGPT Roles (Application: AI CISO)Enhancing Output with Templates (Application: Threat Report)Formatting Output as a Table (Application: Security Controls Table)Setting the OpenAI API Key as an Environment VariableSending API Requests and Handling Responses with PythonUsing Files for Prompts and API Key Access Using Prompt Variables (Application: Manual Page Generator)Getting the most out of this book – get to know your free benefits
Unlock exclusive free benefits that come with your purchase, thoughtfully crafted to supercharge your learning journey and help you learn without limits.
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Technical requirements
For this chapter, you will need a web browser and a stable internet connection to access the ChatGPT platform and set up your account. Basic familiarity with the Python programming language and working with the command line is necessary as you’ll be using Python 3.x, which needs to be installed on your system so that you can work with the OpenAI GPT API and create Python scripts. A code editor will also be essential for writing and editing Python code and prompt files as you work through the recipes in this chapter.
The code files for this chapter can be found here: https://github.com/PacktPublishing/ChatGPT-for-Cybersecurity-Cookbook.
Setting up a ChatGPT Account
In this recipe, we will learn about generative AI, LLMs, and ChatGPT. Then, we will guide you through the process of setting up an account with OpenAI and exploring the features it offers.
Getting ready
To set up a ChatGPT account, you will need an active email address and a modern web browser.
Important note
Every effort has been made to ensure that every illustration and instruction is correct at the time of writing. However, this is such a fast-moving technology and many of the tools used in this book are currently being updated at a rapid pace. Therefore, you might find slight differences.
How to do it…
By setting up a ChatGPT account, you’ll gain access to a powerful AI tool that can greatly enhance your cybersecurity workflow. In this section, we’ll walk you through the steps of creating an account, allowing you to leverage ChatGPT’s capabilities for a range of applications, from threat analysis to generating security reports:
Visit the OpenAI website at https://platform.openai.com/ and click Sign up.Enter your email address and click Continue. Alternatively, you can register with your existing Google or Microsoft account:Figure 1.1 – OpenAI signup form
Enter a strong password and click Continue.Check your email for a verification message from OpenAI. Click the link provided in the email to verify your account.Once your account has been verified, enter the required information (first name, last name, optional organization name, and birthday) and click Continue.Enter your phone number to verify by phone and click Send code.When you receive the text message with the code, enter the code and click Continue.Visit and bookmark https://platform.openai.com/docs/ to start becoming familiar with OpenAI’s documentation and features.How it works…
By setting up an account with OpenAI, you gain access to the ChatGPT API and other features offered by the platform, such as Playground and all available models. This enables you to utilize ChatGPT’s capabilities in your cybersecurity operations, enhancing your efficiency and decision-making process.
There’s more…
When you sign up for a free OpenAI account, you get $18 in free credits. While you most likely won’t use up all of your free credits throughout the recipes in this book, you will eventually with continued use. Consider upgrading to a paid OpenAI plan to access additional features, such as increased API usage limits and priority access to new features and improvements:
Upgrading to ChatGPT Plus:ChatGPT Plus is a subscription plan that offers additional benefits beyond free access to ChatGPT. With a ChatGPT Plus subscription, you can expect faster response times, general access to ChatGPT even during peak times, and priority access to new features and improvements (this includes access to GPT-4 at the time of writing). This subscription is designed to provide an enhanced user experience and ensure that you can make the most out of ChatGPT for your cybersecurity needs.
Benefits of having an API key:Having an API key is essential for utilizing ChatGPT’s capabilities programmatically through the OpenAI API. With an API key, you can access ChatGPT directly from your applications, scripts, or tools, enabling more customized and automated interactions. This allows you to build a wide range of applications, integrating ChatGPT’s intelligence to enhance your cybersecurity practices. By setting up an API key, you’ll be able to harness the full power of ChatGPT and tailor its features to your specific requirements, making it an indispensable tool for your cybersecurity tasks.
Tip
I highly recommend upgrading to ChatGPT Plus so that you have access to GPT-4. While GPT-3.5 is still very powerful, GPT-4’s coding efficiency and accuracy make it more suited to the types of use cases we will be covering in this book and with cybersecurity in general. At the time of writing, there are also other additional features in ChatGPT Plus, such as the availability of plugins and the code interpreter, which will be covered in later chapters.
Creating an API Key and interacting with OpenAI
In this recipe, we will guide you through the process of obtaining an OpenAI API key and introduce you to the OpenAI Playground, where you can experiment with different models and learn more about their capabilities.
Getting ready
To get an OpenAI API key, you will need to have an active OpenAI account. If you haven’t already, complete the Setting up a ChatGPT account recipe to set up your ChatGPT account.
How to do it…
Creating an API key and interacting with OpenAI allows you to harness the power of ChatGPT and other OpenAI models for your applications. This means you’ll be able to leverage these AI technologies to build powerful tools, automate tasks, and customize your interactions with the models. By the end of this recipe, you will have successfully created an API key for programmatic access to OpenAI models and learned how to experiment with them using the OpenAI Playground.
Now, let’s proceed with the steps to create an API key and explore the OpenAI Playground:
Log in to your OpenAI account at https://platform.openai.com.After logging in, click on your profile picture/name in the top-right corner of the screen and select View API keys from the drop-down menu:Figure 1.2 – The API keys screen
Click the + Create new secret key button to generate a new API key.Give your API key a name (optional) and click Create secret key:Figure 1.3 – Naming your API key
Your new API key will be displayed on the screen. Click the copy icon, , to copy the key to your clipboard:Tip
Save your API key in a secure location immediately as you will need it later when working with the OpenAI API; you cannot view the key again in its entirety once it has been saved.
Figure 1.4 – Copying your API key
How it works…
By creating an API key, you enable programmatic access to ChatGPT and other OpenAI models through the OpenAI API. This allows you to integrate ChatGPT’s capabilities into your applications, scripts, or tools, enabling more customized and automated interactions.
There’s more…
The OpenAI Playground is an interactive tool that allows you to experiment with different OpenAI models, including ChatGPT, and their various parameters, but without requiring you to write any code. To access and use the Playground, follow these steps:
Important note
Using the Playground requires token credits; you are billed each month for the credits used. For the most part, this cost can be considered very affordable, depending on your perspective. However, excessive use can add up to significant costs if not monitored.
Log in to your OpenAI account.Click Playground in the top navigation bar:Figure 1.5 – The OpenAI Playground
In the Playground, you can choose from various models by selecting the model you want to use from the Modeldrop-down menu:Figure 1.6 – Selecting a model
Enter your prompt in the textbox provided and click Submit to see the model’s response:Figure 1.7 – Entering a prompt and generating a response
Tip
Even though you are not required to enter an API key to interact with the Playground, usage still counts toward your account’s token/credit usage.
You can also adjust various settings, such as the maximum length, number of generated responses, and more, from the settings panel to the right of the message box:Figure 1.8 – Adjusting settings in the Playground
Two of the most important parameters are Temperature and Maximum length:
The Temperature parameter affects the randomness and creativity of the model’s responses. A higher temperature (for example, 0.8) will produce more diverse and creative outputs, while a lower temperature (for example, 0.2) will generate more focused and deterministic responses. By adjusting the temperature, you can control the balance between the model’s creativity and adherence to the provided context or prompt.The Maximum length parameter controls the number of tokens (words or word pieces) the model will generate in its response. By setting a higher maximum length, you can obtain longer responses, while a lower maximum length will produce more concise outputs. Adjusting the maximum length can help you tailor the response length to your specific needs or requirements.Feel free to experiment with these parameters in the OpenAI Playground or when using the API to find the optimal settings for your specific use case or desired output.
The Playground allows you to experiment with different prompt styles, presets, and model settings, helping you better understand how to tailor your prompts and API requests for optimal results:
Figure 1.9 – Prompt presets and model modes
Tip
While we will be covering several of the different prompt settings using the API throughout this book, we won’t cover them all. You are encouraged to review the OpenAPI documentation for more details.
Basic Prompting (Application: Finding Your IP Address)
In this recipe, we will explore the basics of ChatGPT prompting using the ChatGPT interface, which is different from the OpenAI Playground we used in the previous recipe. The advantage of using the ChatGPT interface is that it does not consume account credits and is better suited for generating formatted output, such as writing code or creating tables.
Getting ready
To use the ChatGPT interface, you will need to have an active OpenAI account. If you haven’t already, complete the Setting up a ChatGPT account recipe to set up your ChatGPT account.
How to do it…
In this recipe, we’ll guide you through using the ChatGPT interface to generate a Python script that retrieves a user’s public IP address. By following these steps, you’ll learn how to interact with ChatGPT in a conversation-like manner and receive context-aware responses, including code snippets.
Now, let’s proceed with the steps in this recipe:
In your browser, go to https://chat.openai.com and click Log in.Log in using your OpenAI credentials.Once you are logged in, you will be taken to the ChatGPT interface. The interface is similar to a chat application, with a text box at the bottom where you can enter your prompts:Figure 1.10 – The ChatGPT interface
ChatGPT uses a conversation-based approach, so you can simply type your prompt as a message and press Enter or click the button to receive a response from the model. For example, you can ask ChatGPT to generate a piece of Python code to find the public IP address of a user:Figure 1.11 – Entering a prompt
ChatGPT will generate a response containing the requested Python code, along with a thorough explanation:
Figure 1.12 – ChatGPT response with code
Continue the conversation by asking follow-up questions or providing additional information, and ChatGPT will respond accordingly:Figure 1.13 – ChatGPT contextual follow-up response
Run the ChatGPT-generated code by clicking on Copy code, paste it into your code editor of choice (I use Visual Studio Code), save it as a .py Python script, and run it froma terminal:PS D:\GPT\ChatGPT for Cybersecurity Cookbook> python .\my_ip.pyYour public IP address is: Your local network IP address is: 192.168.1.105Figure 1.14 – Running the ChatGPT-generated script
How it works…
By using the ChatGPT interface to enter prompts, you can generate context-aware responses and content that continues throughout an entire conversation, similar to a chatbot. The conversation-based approach allows for more natural interactions and the ability to ask follow-up questions or provide additional context. The responses can even include complex formatting such as code snippets or tables (more on tables later).
There’s more…
As you become more familiar with ChatGPT, you can experiment with different prompt styles, instructions, and contexts to obtain the desired output for your cybersecurity tasks. You can also compare the results that are generated through the ChatGPT interface and the OpenAI Playground to determine which approach best fits your needs.
Tip
You can further refine the generated output by providing very clear and specific instructions or using roles. It also helps to divide complex prompts into several smaller prompts, giving ChatGPT one instruction per prompt, building on the previous prompts as you go.
In the upcoming recipes, we will delve into more advanced prompting techniques that utilize these techniques to help you get the most accurate and detailed responses from ChatGPT.
As you interact with ChatGPT, your conversation history is automatically saved in the left panel of the ChatGPT interface. This feature allows you to easily access and review your previous prompts and responses.
By leveraging the conversation history feature, you can keep track of your interactions with ChatGPT and quickly reference previous responses for your cybersecurity tasks or other projects:
Figure 1.15 – Conversation history in the ChatGPT interface
To view a saved conversation, simply click on the desired conversation in the left panel. You can also create new conversations by clicking on the + New chat button located at the top of the conversation list. This enables you to separate and organize your prompts and responses based on specific tasks or topics.
Note of caution
Keep in mind that when you start a new conversation, the model loses the context of the previous conversation. If you want to reference any information from a previous conversation, you will need to include that context in your new prompt.
Applying ChatGPT Roles (Application : AI CISO)
In this recipe, we will demonstrate how you can use roles in your prompts to improve the accuracy and detail of ChatGPT’s responses. Assigning roles to ChatGPT helps it generate more context-aware and relevant content, particularly when you need expert-level insights or recommendations.
Getting ready
Ensure you have access to the ChatGPT interface by logging in to your OpenAI account.
How to do it…
By assigning roles, you’ll be able to obtain expert-level insights and recommendations from the model. Let’s dive into the steps for this recipe:
To assign a role to ChatGPT, start your prompt by describing the role you want the model to assume. For example, you could use the following prompt: You are a cybersecurity expert with 20 years of experience. Explain the importance of multi-factor authentication (MFA) in securing online accounts, to an executive audience.ChatGPT will generate a response that aligns with the assigned role, providing a detailed explanation of the topic based on the expertise and perspective of a cybersecurity expert:Figure 1.16 – ChatGPT response with role-based expertise
Experiment with assigning different roles for different scenarios, such as the following: You are a CISO with 30 years of experience. What are the top cybersecurity risks businesses should be aware of?Alternatively, you can use the following: You are an ethical hacker. Explain how a penetration test can help improve an organization's security posture.Note of caution
Keep in mind that ChatGPT’s knowledge is based on the data it was trained on, which has a cutoff date of September 2021. As a result, the model may not be aware of the latest developments, trends, or technologies in the cybersecurity field that emerged after its training data cutoff. Always verify the information generated by ChatGPT with up-to-date sources and take its training limitations into account when interpreting its responses. We will discuss techniques on how to get around this limitation later in this book.
How it works…
When you assign a role to ChatGPT, you provide a specific context or persona for the model to work with. This helps the model generate responses that are tailored to the given role, resulting in more accurate, relevant, and detailed content. The model will generate content that aligns with the expertise and perspective of the assigned role, offering better insights, opinions, or recommendations.
There’s more…
As you become more comfortable using roles in your prompts, you can experiment with different combinations of roles and scenarios to obtain the desired output for your cybersecurity tasks. For example, you can create a dialogue between two roles by alternating prompts for each role:
Role 1: You are a network administrator. What measures do you take to secure your organization's network?Role 2: You are a cybersecurity consultant. What additional recommendations do you have for the network administrator to further enhance network security?By using roles creatively and experimenting with different combinations, you can leverage ChatGPT’s expertise and obtain more accurate and detailed responses for a wide range of cybersecurity topics and situations.
We will experiment with automating role conversations in later chapters.
Enhancing Output with Templates (Application: Threat Report)
In this recipe, we will explore how to use output templates to guide ChatGPT’s responses, making them more consistent, well-structured, and suitable for reports or other formal documents. By providing a specific format for the output, you can ensure that the generated content meets your requirements and is easier to integrate into your cybersecurity projects.
Getting ready
Ensure you have access to the ChatGPT interface by logging in to your OpenAI account.
How to do it…
To get started, follow these steps:
When crafting your prompt, you can specify the output of several different formatting options, such as headings, font weight, lists, and more. The following prompt demonstrates how to create output with headings, font weights, and list types: Create an analysis report of the WannaCry Ransomware Attack as it relates to the cyber kill chain, using the following format: # Threat Report ## Overview - **Threat Name:** - **Date of Occurrence:** - **Industries Affected:** - **Impact:** ## Cyber Kill Chain Analysis 1. **Kill chain step 1:** 2. **Kill chain step 2:** 3. … ## Mitigation Recommendations - *Mitigation recommendation 1* - *Mitigaiton recommendation 2* …ChatGPT will generate a response that follows the specified template, providing a well-structured and consistent output:Figure 1.17 – ChatGPT response with formatting (headings, bold font, and lists)
Figure 1.18 – ChatGPT response with formatting (heading, lists, and italicized text)
This formatted text is now more structured and can be easily transferred to other documents through copying and pasting while retaining its formatting.How it works…
By providing a clear template for the output in your prompt, you guide ChatGPT to generate responses that adhere to the specified structure and formatting. This helps ensure that the generated content is consistent, well organized, and suitable for use in reports, presentations, or other formal documents. The model will focus on generating content that matches the output template formatting and structure you’ve provided while still delivering the information you requested.
The following conventions are used when formatting ChatGPT output:
To create a main heading, use a single pound sign (#), followed by a space and the text of the heading. In this case, the main heading is Threat Report.To create a subheading, use two pound signs (##), followed by a space and the text of the subheading. In this case, the subheadings are Overview, Cyber Kill Chain Analysis, and Mitigation Recommendations. You can continue to create additional subheading levels by increasing the number of pound signs.To create bullet points, use a hyphen (-) or asterisk (*), followed by a space and the text of the bullet point. In this case, the bullet points are used in the Overview section to indicate the threat’s name, date of occurrence, industries affected, and impact.To create bold text, use two asterisks (**) or underscores (__) to surround the text you want to bold. In this case, each of the bullets and numbered list keywords were bolded.To italicize text, use a pair of asterisks (*) or underscores (_) to surround the text you want to italicize. In this case, the second kill chain step is italicized using a pair of underscores. Here, italicized text is used for the mitigations recommendations bullets.To create a numbered list, use a number followed by a period and a space, followed by the text of the list item. In this case, the Cyber Kill Chain Analysis section is a numbered list.There’s more…
Combining templates with other techniques, such as roles, can further enhance the quality and relevance of the generated content. By applying both templates and roles, you can create output that is not only well-structured and consistent but also tailored to specific expert perspectives.
As you become more comfortable using templates in your prompts, you can experiment with different formats, structures, and scenarios to obtain the desired output for your cybersecurity tasks. For example, in addition to text formatting, you can also use tables to organize the generated content even further, which is what we will cover in the next recipe.
Formatting Output as a Table (Application: Security Controls Table)
In this recipe, we will demonstrate how to create prompts that guide ChatGPT to generate output in table format. Tables can be an effective way to organize and present information in a structured and easy-to-read manner. In this example, we will create a security controls comparison table.
Getting ready
Ensure you have access to the ChatGPT interface by logging into your OpenAI account.
How to do it…
This example will demonstrate how to create a security controls comparison table. Let’s dive into the steps to achieve this:
Craft your prompt by specifying the table format and the information you want to include. For this example, we will generate a table comparing different security controls: Create a table comparing five different security controls. The table should have the following columns: Control Name, Description, Implementation Cost, Maintenance Cost, Effectiveness, and Ease of Implementation.ChatGPT will generate a response containing a table with the specified columns, populated with relevant information:Figure 1.19 – Snippet of a ChatGPT response with a table
You can now easily copy and paste the generated table directly into a document or spreadsheet, where it can be further formatted and refined:Figure 1.20 – ChatGPT response copied/pasted directly into a spreadsheet
How it works…
By specifying the table format and required information in your prompt, you guide ChatGPT to generate content in a structured, tabular manner. The model will focus on generating content that matches the specified format and populating the table with the requested information. The ChatGPT interface automatically understands how to provide table formatting using markdown language, which is then interpreted by the browser.
In this example, we asked ChatGPT to create a table comparing five different security controls with columns for Control Name, Description, Implementation Cost, Maintenance Cost, Effectiveness, and Ease of Implementation. The resulting table provides an organized and easy-to-understand overview of the different security controls.
There’s more…
As you become more comfortable using tables in your prompts, you can experiment with different formats, structures, and scenarios to obtain the desired output for your cybersecurity tasks. You can also combine tables with other techniques, such as roles and templates, to further enhance the quality and relevance of the generated content.
By using tables creatively and experimenting with different combinations, you can leverage ChatGPT’s capabilities to generate structured and organized content for various cybersecurity topics and situations.
Setting the OpenAI API Key as an Environment Variable
In this recipe, we will show you how to set up your OpenAI API key as an environment variable. This is an essential step as it allows you to use the API key in your Python code without hardcoding it, which is a best practice for security purposes.
Getting ready
Ensure that you have already obtained your OpenAI API key by signing up for an account and accessing the API key section, as outlined in the Creating an API key and interacting with OpenAI recipe.
How to do it…
This example will demonstrate how to set up your OpenAI API key as an environment variable for secure access in your Python code. Let’s dive into the steps to achieve this.
Set up the API key as an environment variable on your operating system.
