From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

By Maanak Gupta, Charankumar Akiri, Kshitiz Aryal, Elisabeth Parker, Lopamudra Praharaj. IEEE Access, 2023

654 citations recorded by the source corpus.

Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it’s critical to understand its consequences from a cybersecurity perspective. Several instances recently have demonstrated the use of GenAI tools in both the defensive and offensive side of cybersecurity, and focusing on the social, ethical and privacy implications this technology possesses. This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy. The work presents the vulnerabilities of ChatGPT, which can be exploited by malicious users to exfiltrate malicious information bypassing the ethical constraints on the model. This paper demonstrates successful example attacks like Jailbreaks, reverse psychology, and prompt injection attacks on the ChatGPT. The paper also investigates how cyber offenders can use the GenAI tools in developing cyber attacks, and explore the scenarios where ChatGPT can be used by adversaries to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. This paper then examines defense techniques and uses GenAI tools to improve security measures, including cyber defense automation, reporting, threat intelligence, secure code generation and detection, attack identification, developing ethical guidelines, incidence response plans, and malware detection. We will also discuss the social, legal, and ethical implications of ChatGPT. In conclusion, the paper highlights open challenges and future directions to make this GenAI secure, safe, trustworthy, and ethical as the community understands its cybersecurity impacts.

Subfields: Artificial Intelligence, Building and Construction, Information Systems

View Paper: https://www.semanticscholar.org/paper/317ad53bea6fb603c20f692bb2f1a01e2dc86161

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Duen Horng Chau
2. Sebastian Szyller

In Information Systems

1. Duen Horng Chau
2. Binghui Wang
3. Neil Zhenqiang Gong

Citations in the corpus

• A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly (2023, background): “A number of surveys (e.g., [91, 58, 238, 49, 59, 219, 231, 232, 7]) highlight the threats and attacks against LLMs.”
• LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked (2023, background): “However, despite their abilities to produce positive content, LLMs can also generate harmful material like phishing emails, malicious code, and hate speech [10, 28].”