OpenAI’s ChatGPT, released in November 2022, stunned users with its diverse capabilities, answering questions and crafting custom essays, sparking widespread fascination.
The versatility of ChatGPT significantly excels in addressing inquiries across diverse domains, drawing attention to its remarkable information analysis, comprehension, and synthesis from various sources and user inputs.
Following cybersecurity researchers from The Pennsylvania State University, United States, recently published a research analysis on ChatGPT for software security:-
- Zhilong Wang
- Lan Zhang
- Peng Liu
ChatGPT for Software Security
ChatGPT captivates researchers and users with its versatile domain expertise, but its evolving applications and potential risks deserve closer inspection.
While the users who are unaware may fall prey to ChatGPT’s misleading outputs, as seen in experts encountering fake or unreliable paper recommendations.
OpenAI’s GPT-4 Technical Report highlights impressive achievements, passing a simulated bar exam with human-level proficiency.
However, ChatGPT’s limitations persist, challenging to address due to plausible but incorrect answers and the lack of a definitive truth source during RL training.
The recent papers explore ChatGPT’s strengths and failures, including mathematical and coding tasks. A case study digs into its capacity in software security, focusing on analysis abilities rather than generative skills.
Enhancement of Cybersecurity Using AI
Cybersecurity relies on manual processes like reverse engineering and vulnerability analysis. AI and deep learning offer promising solutions to enhance threat detection, prediction, and automation for security teams.
Deep learning enhances security program analysis with broad accessibility and versatile applications, including vulnerability discovery, fixing, and strengthening software resilience.
CodeBert and GraphCodeBERT, pre-trained models based on Transformers, enable effective source code analysis and protection, learning code representations from large-scale unlabeled data across six programming languages.
The applications of deep learning in program analysis are categorized into two main groups by the security researchers and here they are mentioned below:-
- Deep learning for source code analysis.
- Deep learning for binary analysis.
ChatGPT excels in source code analysis, enabling security experts to discover and fix vulnerabilities efficiently.
Large language models like ChatGPT revolutionize security source code analysis, efficiently learning high-level semantics from well-renowned source code.
ChatGPT surpasses CodeBert and GraphCodeBert, excelling in security source code analysis, even at the binary level, with impressive learning capabilities for low-level semantics.
While ChatGPT excels in source code analysis, it has limitations in cases of insufficient naming information and precision in specific implementation-level questions, highlighting areas for further improvement.
Other ChatGPT Resources:
- ChatGPT for Digital Forensic – AI-Powered Cybercrime Investigation
- PentestGPT – A ChatGPT Empowered Automated Penetration Testing Tool
- ChatGPT For Penetration Testing – An Effective Reconnaissance Phase of Pentest
- ChatGPT to ThreatGPT: Generative AI Impact in Cybersecurity and Privacy