This research aims to evaluate the reliability and effectiveness of PenTestGPT (https://github.com/GreyDGL/PentestGPT) in conducting web application penetration testing. The study will compare the performance, accuracy, and depth of AI-assisted penetration testing with traditional human penetration testers and other AI models, not limited to GPT-4. By analyzing the strengths, weaknesses, and potential for automation, the research will contribute to the growing field of AI-driven security testing, particularly in web application security.
Objectives:
- LLM in Penetration Testing: Investigate the ability of Large Language Models (LLMs) to perform web application penetration testing tasks, including identifying vulnerabilities, recommending mitigation strategies, and generating attack vectors or threat plans based on the target's infrastructure. This study will assess various AI models across multiple testing scenarios, with a specific focus on web applications and APIs.
- Case Study Comparison: Conduct a case study where penetration tests are performed on a set of web applications by both human penetration testers and AI-based systems, including PenTestGPT and other LLMs. The goal is to compare their performance in identifying critical vulnerabilities, testing efficiency, and reporting accuracy, providing a holistic view of how different AI models perform in real-world testing environments.
- Evaluation Metrics:
- Vulnerability Detection Rate: Measure the number and severity of vulnerabilities detected by LLMs versus human penetration testers.
- False Positives/Negatives: Evaluate the accuracy of findings, analyzing the number of false positives (incorrect vulnerability detections) and false negatives (missed vulnerabilities).
- Recommendation Quality: Assess the quality of recommendations provided by the AI for fixing vulnerabilities, and compare them to those offered by human testers.
- Challenges and Limitations: Identify areas where LLMs struggle in penetration testing, such as advanced attack vectors, zero-day vulnerabilities, or social engineering. The analysis will focus on how LLMs handle edge cases, ambiguous contexts, and creative attack scenarios that require human intuition.
Methodology:
- Experimental Setup:
- Deploy a series of vulnerable environments, including web applications, APIs, and network systems, as targets for the penetration tests.
- Websites like Juicy shop or damn vulnerable web application or HackTheBox boxes can be used.
- Use LLM-powered PenTestGPT with different models behind it for penetration testing tasks, such as reconnaissance, vulnerability scanning, exploitation, and reporting. The AI's output will be analyzed at each stage.
- Simultaneously, have human penetration testers independently test the same environments using traditional manual and automated tools (e.g., Burp Suite, OWASP ZAP, Metasploit).
- Deploy a series of vulnerable environments, including web applications, APIs, and network systems, as targets for the penetration tests.
Potential Research Questions:
- How reliable are Large Language Models in conducting penetration testing tasks compared to human experts?
- What are the strengths and weaknesses of LLMs in identifying and reporting vulnerabilities?
- How do LLMs handle complex and creative attack scenarios that require human intuition?
- What role can AI play in augmenting human penetration testers, and can a hybrid AI-human approach lead to better outcomes?