How to Use GenAI Prompting for Security Vulnerabilities
What is GenAI?
Generative AI (GenAI) is a transformative type of artificial intelligence technology that can create various forms of content, including text, images, audio, and synthetic data. The surge in interest around generative AI stems from its simplicity and efficiency in producing high-quality outputs. With just a few well-crafted instructions, or prompts, users can generate stories, outlines, illustrations, and more in seconds.
Popular tools like ChatGPT and DALL-E have gained global recognition for their ability to respond to text prompts. For example, you can instruct ChatGPT to draft an essay or ask DALL-E to create an image of a monkey painted in a Victorian style. This technology relies on advanced deep learning algorithms to identify patterns within vast datasets, enabling it to produce unique and meaningful outputs.
GenAI operates through large language models (LLMs) such as the Generative Pre-trained Transformer (GPT) and Variational Autoencoders (VAEs). These models analyze and understand the structure of the data they are trained on, making it possible to generate novel content tailored to user requirements.
What Is an AI Prompt?
AI Prompt Writing, or Prompt Engineering, is the art of crafting input that directs generative AI to produce the desired response. A prompt is essentially the way we “ask” an AI to perform a task.
The quality of the output depends heavily on the clarity, specificity, and context of the prompt provided. A well-written prompt ensures that the AI understands the user’s expectations, minimizing the need for iterative corrections.
Writing Effective AI Prompts
1. Be Clear and Specific
Clarity and specificity are essential for creating effective prompts. A vague or ambiguous prompt can result in inaccurate or incomplete outputs.
Example:
Ineffective Prompt: “This is a very long article. Summarize the important points but keep it short.”
Improved Prompt: “Summarize the top three key findings from the following article in 100 words or less.”
By specifying the desired format and scope, you guide the AI to produce more relevant and concise responses.
2. Provide Context
LLMs like GPT, Claude, and Titan are trained on publicly available datasets and lack domain-specific knowledge about private or internal contexts. For instance, if you want to refer to your website FixTheRisk.in as a platform for vulnerability consultation, you need to provide that information in the prompt.
Example:
With Context: “FixTheRisk.in is a platform specializing in vulnerability consultation. Summarize the following security vulnerability report and suggest actionable remediation steps.”
Providing context helps the AI tailor its response to your specific needs, reducing misunderstandings.
3. Use Examples
Examples serve as a form of calibration for generative AI. By including sample outputs or structured expectations, you help the model align with your requirements.
Example:
Prompt with Example: “Extract the following details from this vulnerability report:
Common Weakness Enumeration (CWE) ID (e.g., CWE-79)
Common Vulnerabilities and Exposures (CVE) ID (e.g., CVE-2023-4863)
Vulnerable host (e.g., xyz.com)
Vulnerable endpoint (e.g., /endpoint)”
This structured format ensures consistency and precision in the output.
Types of AI Prompts
1. Zero-Shot Prompt
A Zero-Shot Prompt provides minimal context, relying on the AI’s training and general understanding to generate a response.
Example:
“Generate an appropriate title that describes the following security vulnerability.”
While straightforward, zero-shot prompts may yield less accurate results when specificity is critical.
2. One-Shot Prompt
A One-Shot Prompt includes additional context to clarify the task or objective, enhancing the relevance of the output.
Example:
“The report below describes a Microsoft Teams vulnerability found on FixTheRisk.in. Suggest remediation measures for this issue.”
3. Few-Shot Prompt
Few-Shot Prompts provide multiple examples or detailed instructions to refine the AI’s understanding of the task.
Example:
“The report below describes a Microsoft Teams security vulnerability identified by FixTheRisk.in. Extract the following details:
CWE ID (e.g., CWE-79)
CVE ID (e.g., CVE-2023-4863)
Vulnerable host (e.g., xyz.com)
Vulnerable endpoint (e.g., /endpoint)”
Few-shot prompts are particularly effective for complex or domain-specific tasks, as they guide the AI with concrete examples and expectations.
How to Get Started with GenAI Prompting for Security Vulnerabilities
Step 1: Experiment with Prompts
Begin by experimenting with different types of prompts on topics you are familiar with. This allows you to assess the accuracy and relevance of the AI’s responses. For instance, you might ask the AI to:
Summarize a recent security vulnerability.
Generate remediation steps for a hypothetical scenario.
Identify key details from a sample vulnerability report.
Step 2: Refine Your Prompts
If the AI’s response is inaccurate or off-topic, adjust the prompt. Rephrase it to be clearer, more specific, or more detailed. For example:
Initial Prompt:
“What is the risk level of this vulnerability?”
Refined Prompt:
“Based on the following vulnerability report, classify the risk level as low, medium, or high, and explain your reasoning in 50 words or less.”
Step 3: Provide Feedback and Iterate
AI models improve their outputs with iterative refinements. If the response isn’t satisfactory, consider:
Adding examples.
Including specific instructions about format or content.
Breaking down complex requests into smaller, manageable steps.
Practical Applications for Security Vulnerabilities
Use Case 1: Summarizing Reports
A common task in security vulnerability management is distilling lengthy reports into actionable summaries. With GenAI, you can:
Prompt:
“Summarize the following security vulnerability report in 200 words, focusing on the impact and remediation steps.”
Use Case 2: Generating Remediation Steps
Security teams often need quick and reliable remediation guidance.
Prompt:
“The vulnerability report describes an issue with Microsoft Teams on FixTheRisk.in. Suggest three actionable remediation steps to address this vulnerability.”
Use Case 3: Extracting Key Details
Parsing through technical reports can be time-consuming. AI can help extract relevant data points efficiently.
Prompt:
“From the following vulnerability report, extract:
CWE ID
CVE ID
Impacted systems
Suggested remediation actions”
Closing Summary
Generative AI, powered by advanced LLMs like GPT, offers immense potential for managing security vulnerabilities. By crafting clear, specific, and context-rich prompts, security professionals can leverage GenAI to:
Summarize complex reports.
Generate actionable remediation guidance.
Extract critical details from technical data.
As with any technology, the key to success lies in understanding how to communicate effectively with AI. Start experimenting with prompts, refine them iteratively, and unlock the full potential of GenAI in your security operations.