Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations
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Abstract (arXiv)
Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity threats and vulnerabilities. This paper discussed the relevant literature, reports, and explanatory incident attacks generated against chatbots. Our initial point is to explore the timeline of chatbots from ELIZA (an early natural language processing computer program) to GPT-4 and provide the working mechanism of ChatGPT. Subsequently, we explored the cybersecurity attacks and vulnerabilities in chatbots. Besides, we investigated the ChatGPT, specifically in the context of creating the malware code, phishing emails, undetectable zero-day attacks, and generation of macros and LOLBINs. Furthermore, the history of cyberattacks and vulnerabilities exploited by cybercriminals are discussed, particularly considering the risk and vulnerabilities in ChatGPT. Addressing these threats and vulnerabilities requires specific strategies and measures to reduce the harmful consequences. Therefore, the future directions to address the challenges were presented.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 115 references that are in the map. First posted on arXiv 29 May 2023.
Builds on
29 of 115 references.
References · 29
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023GPT-4 Technical Reportcited by 18,132
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2019Defending Against Neural Fake Newscited by 553
- 2023Mathematical Capabilities of ChatGPTcited by 263
- 2017Recent Trends in Deep Learning Based Natural Language Processingcited by 262
- 2017A Survey on Dialogue Systems: Recent Advances and New Frontierscited by 162
- 2015Neural Responding Machine for Short-Text Conversationcited by 124
- 2018Adversarial Reprogramming of Neural Networkscited by 115
17 more
- 2015A Neural Network Approach to Context-Sensitive Generation of Conversational Responsescited by 98
- 2017DolphinAtack: Inaudible Voice Commandscited by 65
- 2022Targeted Phishing Campaigns using Large Scale Language Modelscited by 22
- 2014An Information Retrieval Approach to Short Text Conversationcited by 18
- 2020Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancementscited by 17
- 2021Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Differencecited by 13
- 2013Artificial Intelligence MArkup Language: A Brief Tutorialcited by 7
- 2017The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Studycited by 4
- 2022An Empirical Assessment of Security and Privacy Risks of Web based-Chatbotscited by 4
- 2023Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistantscited by 4
- 2017Digital Forensic Approaches for Amazon Alexa Ecosystemcited by 3
- 2020Seq2Seq AI Chatbot with Attention Mechanismcited by 3
- 2017Community Targeted Phishing: A Middle Ground Between Massive and Spear Phishing through Natural Language Generationcited by 2
- 2019Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replaycited by 2
- 2022Living-off-the-Land Abuse Detection Using Natural Language Processing and Supervised Learningcited by 2
- 2023Hello Me, Meet the Real Me: Audio Deepfake Attacks on Voice Assistantscited by 2
- 2022Zero Botnets: An Observe-Pursue-Counter Approachcited by 1
Built on by
Citing papers per year: 2023: 4; 2024: 2; 2025: 1.
7 papers.
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
- 2023RatGPT: Turning online LLMs into Proxies for Malware Attackscited by 17
- 2023Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directionscited by 16
- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awarenesscited by 14
- 2023The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Erniecited by 5
- 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systemscited by 1
- 2024GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awarenesscited by 0
- 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems29 Dec 2025
- 2024GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness7 May 2024
- 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness26 Jan 2024
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly4 Dec 2023
- 2023The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie5 Sep 2023
- 2023RatGPT: Turning online LLMs into Proxies for Malware Attacks17 Aug 2023
- 2023Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions8 Aug 2023