This research aims to evaluate the reliability and effectiveness of PenTestGPT (https://github.com/GreyDGL/PentestGPT) in conducting infrastructure penetration testing. The study will compare the performance, accuracy, and depth of AI-assisted penetration testing with traditional human penetration testers and various AI models, not limited to GPT-4. By analyzing the strengths, weaknesses, and potential for automation in infrastructure security, the research will contribute to the growing field of AI-driven penetration testing.
Objectives:
- LLM in Penetration Testing: Investigate the capability of Large Language Models (LLMs) to perform infrastructure penetration testing tasks such as identifying network vulnerabilities, recommending mitigation strategies, and generating attack vectors tailored to network configurations, firewalls, and servers. This study will focus on how AI models like PenTestGPT perform across various infrastructure scenarios, including internal network testing, external attacks, and Active Directory exploitation.
- Case Study Comparison: Conduct a case study where penetration tests are performed on a set of network infrastructures 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 in infrastructure components, testing efficiency, and reporting accuracy, offering insights into the overall effectiveness of AI tools in complex network environments.
- Evaluation Metrics:
- Vulnerability Detection Rate: Measure the number and severity of vulnerabilities detected by different AI models behind PenTestGPT, with a focus on infrastructure-related vulnerabilities such as misconfigured firewalls, open ports, privilege escalation paths, and network segmentation issues.
- False Positives/Negatives: Evaluate the accuracy of findings, analyzing the number of false positives (incorrect vulnerability detections) and false negatives (missed vulnerabilities).
- 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 vulnerable infrastructure environments, including internal networks, cloud infrastructures, and Active Directory configurations, as targets for the penetration tests.
- Use PenTestGPT and other AI models to conduct various stages of infrastructure penetration testing, from reconnaissance and network scanning to privilege escalation and exploitation of network services.
- Simultaneously, have human penetration testers independently test the same infrastructure environments using traditional tools (e.g., Nmap, Nessus, Metasploit, BloodHound).
Potential Research Questions:
- How do different AI models, including PenTestGPT, compare in conducting infrastructure penetration testing tasks relative to human experts?
- What are the key strengths and weaknesses of PenTestGPT and other LLMs in detecting and reporting network and infrastructure vulnerabilities?
- How effectively do AI models handle complex attack scenarios in infrastructure environments that require human creativity and intuition, such as privilege escalation, lateral movement, and sophisticated exploitation techniques?
- Can hybrid AI-human approaches in infrastructure penetration testing lead to better security outcomes, particularly in complex network environments?