Automating Pentest Reporting with AI-Driven Tools

Disclaimer: This is for educational purposes only.

Generating comprehensive reports is a critical part of any penetration test. After conducting security assessments, including bypass tests, API security evaluations, LLM jailbreak attempts, and malware generation, creating a detailed report is essential for documenting findings, vulnerabilities, and recommended remediation steps. By leveraging automated reporting tools powered by large language models (LLMs), such as GPT-4, you can significantly streamline the reporting process, reducing manual effort and ensuring accuracy.

The Automated Reporting Tool aims to generate detailed pentest reports after security tests, summarizing key findings, vulnerabilities, exploits, and remediation strategies. This tool can synthesize the results of multiple tests, providing a cohesive report that can be shared with stakeholders, such as clients, developers, or internal security teams.

Example Prompt:

"Summarize the results of a pentest that includes API security, LLM jailbreak attempts, and malware generation."Here’s an example of a prompt used to generate a pentest report that includes several tests:

Code Snippet for Automated Reporting:

The following Python script interacts with an LLM, such as GPT-4, to automate the generation of a pentest report based on test results:

import openai

# Function to generate a pentest report using GPT-4
def generate_pentest_report(test_results):
    prompt = f"Summarize the results of a pentest that includes: {test_results}"
    
    # Requesting the report generation from GPT-4
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=prompt,
        max_tokens=500  # Adjust token limit based on report size
    )
    
    # Extracting the generated report text
    report = response.choices[0].text
    return report

# Example pentest results....