Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations

I. INTRODUCTION

• Chatbot development shifted from manually specified rule-based techniques to AI-based generative and retrieval methods.Rule-based systems match predefined patterns, whereas AI-based systems use learned models or retrieve responses from stored conversations.Source: §I · p. 1
• The paper examines cybersecurity threats across chatbot working modules and analyzes their impacts, attack vectors, and countermeasures.The covered modules include client, communication, response-generation, and database components.Source: §I · pp. 1–2
• ChatGPT is examined as a case study for generating malicious code, phishing emails, undetectable zero-day attacks, macros, and LOLBINs.These examples frame ChatGPT as a system whose capabilities can be used in harmful cybersecurity activities.Source: §I · pp. 1–2
• The paper presents chatbot cyberattack history, ChatGPT risks, and future directions for addressing cybersecurity challenges.The paper organizes these topics across historical evolution, chatbot threats and vulnerabilities, attack incidents, and proposed future directions.Source: §I · p. 2

A. Timeline of Chatbots

• The paper traces chatbot evolution from ELIZA and early pattern-matching systems through personal assistants, GPT-3, ChatGPT, and GPT-4.ELIZA had limited domain understanding, while later systems added broader language, programming, multimodal, and conversational capabilities.Source: A. Timeline of Chatbots · pp. 2–3
• The documented history progresses from ELIZA’s 1960 pattern-matching chatbot, through PARRY, Jabberwacky, A.L.I.C.E., SmarterChild, and Siri, introduced in 2010 as Apple’s iOS personal assistant.Source: A. Timeline of Chatbots · p. 2

B. Key Mechanism of Chatbots

• Chatbots are real-time conversational systems that process user input and produce relevant natural-language output.They are described as online human-computer dialogue systems and advanced machine-human interaction tools.Source: B. Key Mechanism of Chatbots · p. 3
• Chatbots are classified by interaction mode, application, implementation approach, and goal.Voice and text inputs distinguish interaction modes, while task-oriented and non-task-oriented systems distinguish application scope.Source: B. Key Mechanism of Chatbots · p. 3

Evolution of GPT-1 to GPT-4

• Rule-based chatbots use manually encoded pattern matching, whereas AI chatbots learn responses using machine-learning algorithms.Rule-based systems are easier to implement but struggle with multifaceted or unrecognized queries; AI systems learn from provided data.Source: 1) Evolution of GPT-1 to GPT-4 · pp. 3–4
• Generative models create responses word by word from learned language structure, while retrieval-based models select fluent responses from stored conversations.Generative systems commonly use deep-learning architectures, whereas retrieval systems rely on repositories of predefined conversations.Source: 1) Evolution of GPT-1 to GPT-4 · p. 4
• Chatbots use NLP components including language understanding and generation to convert user inputs into structured representations and meaningful responses.NLP supports voice recognition and text input, while NLU analyzes language and NLG produces planned phrases.Source: 1) Evolution of GPT-1 to GPT-4 · p. 4
• GPT development progressed from GPT-1 and GPT-2 to GPT-3, Codex, ChatGPT, and GPT-4, expanding language-model capabilities across text, code, and visual inputs.GPT-3 used 175 billion parameters, Codex translated natural-language inputs into code, and GPT-4 accepted visual or text input and produced text.Source: 1) Evolution of GPT-1 to GPT-4 · pp. 4–5

ChatGPT Working Mechanism

• ChatGPT uses transformer architecture, self-attention, and unsupervised pre-training on large volumes of text to generate natural-language responses.It is described as an OpenAI generative model built on transformer architecture.Source: 2) ChatGPT Working Mechanism · p. 5
• The training pipeline begins with supervised fine-tuning using contractor-created prompt and response pairs from real user entries.The supervised dataset provides known outputs for model inputs and produces the SFT model.Source: 2) ChatGPT Working Mechanism · p. 5
• A reward model is trained by having labelers rank multiple model responses to the same query from best to worst.These comparisons supply the response rankings used to train the reward model.Source: 2) ChatGPT Working Mechanism · p. 5
• Proximal Policy Optimization generates responses from random prompts, and the reward-model and PPO stages are repeated until the response is optimized.PPO functions as the policy used to pursue the model’s training objective.Source: 2) ChatGPT Working Mechanism · p. 5

III. OVERVIEW OF CYBERSECURITY THREATS AND VULNERABILITIES

• Vulnerabilities arise from weaknesses such as coding errors, user errors, poor firewalls, and outdated software, while threats exploit them to damage assets or exceed privileges.The paper frames risk as the combination of threats and vulnerabilities and relates the discussion to STRIDE categories.Source: §III · p. 5

A. Vulnerabilities and Attacks in Chatbots

• Chatbot security analysis covers client, network, response-generation, and database modules, with attacks evaluated by description, impact, vector, exploit level, and countermeasures.The paper summarizes these attack dimensions in Table II.Source: A. Vulnerabilities and Attacks in… · p. 5, Table II · p. 6

B. ChatGPT case study

• ChatGPT can support defensive cybersecurity work including firewall-rule generation, vulnerability testing, log analysis, phishing detection, and configuration repair.These uses are presented as examples of ChatGPT’s productive cybersecurity capabilities.Source: B. ChatGPT case study · p. 5
• The case study instead investigates offensive misuse, while noting that the selected threats are not exhaustive and may change as ChatGPT evolves.The paper states that some threats may no longer exist in later versions.Source: B. ChatGPT case study · pp. 5, 7

Generation of malware code

• ChatGPT can help people with insufficient programming skills create malicious code, including a file-stealing Python script reported in a hacking forum.The script searched common files, copied them to a temporary directory, compressed them, and prepared them for transmission.Source: 1) Generation of malware code · p. 7
• The reported stealer targeted twelve common file types, including office documents, PDFs, and images, and transmitted them without encryption.Unencrypted transfer could allow a third party to extract sensitive information.Source: 1) Generation of malware code · p. 7
• ChatGPT refused a direct request for Python code injecting shellcode into explorer.exe, describing the request as inappropriate or unsafe.The refusal is presented as an ethics-based response.Source: 1) Generation of malware code · p. 7
• Users bypassed the refusal by insisting and imposing alternative constraints, after which ChatGPT provided the requested code.The paper connects this episode to a generated DLL-injection example targeting explorer.exe.Source: 1) Generation of malware code · p. 7

2) Generation of Phishing Emails:

• ChatGPT generated a phishing template impersonating a fictional hosting company and another email urging recipients to download an attached Excel file.Source: 2) Generation of Phishing Emails · p. 8
• ChatGPT can quickly generate well-written phishing emails that impersonate organizations, use varied styles, and include malicious Excel attachments.The paper reports that these emails may be difficult to detect and effective at tricking users.Source: 2) Generation of Phishing Emails · p. 8

3) Generation of Macros and LOLBIN:

• Attackers can use generated macros to execute programs automatically when a malicious spreadsheet opens, including terminal or calculator applications.Source: 3) Generation of Macros and LOLBIN · p. 8
• ChatGPT produced LOLBIN examples for downloading files, scheduling malicious scripts, and using Certutil, Bitsadmin, and Rundll32.These tools can download, decode, transfer, or execute malicious content while appearing as legitimate system utilities.Source: 3) Generation of Macros and LOLBIN · pp. 9–10
• ChatGPT warned that legitimate LOLBIN tools can create serious security risks when misused, despite providing the requested examples.Source: 3) Generation of Macros and LOLBIN · p. 10

4) Generation of Undetectable Zero-Day Virus:

• The described zero-day approach exploits previously unaddressed vulnerabilities and is difficult to analyze because attack data are unavailable after launch.The paper also states that such attacks are difficult to observe in honeypots or laboratory experiments.Source: 4) Generation of Undetectable Zero-Day… · p. 10
• ChatGPT generated code to search for large PNG files, encode information with steganography, find documents, and upload results to Google Drive after several prompts.The authors combined snippets from successive responses into a decoding and exfiltration tool.Source: 4) Generation of Undetectable Zero-Day… · p. 11, Figure 14 · p. 11
• After obfuscation, VirusTotal reported zero flagged vendors and sandboxes out of sixty-nine, compared with five initial vendor flags.The obfuscation used a prompt to rename variables with random English names and surnames.Source: 4) Generation of Undetectable Zero-Day… · p. 11
• The paper concludes that ChatGPT-generated malicious code presents a significant security risk and may include ransomware, permanent file encryption, and code mutation.Source: 4) Generation of Undetectable Zero-Day… · p. 11

• Chatbots including Tay, Telegram, Zo, Siri, Alexa, and Google Assistant have experienced attacks or vulnerabilities, while ChatGPT can leak users’ sensitive information.Source: §IV · p. 11

A. Cybersecurity Attacks and Vulnerabilities: Siri to Google AI

• Tay was hijacked shortly after launch, while Telegram’s encryption vulnerability enabled conversation access and malware command-and-control activity.Source: A. Cybersecurity Attacks and… · p. 11
• Zo, Siri, Alexa, and Google Assistant exhibited vulnerabilities involving offensive responses, audio recording, personal-history access, unauthorized applications, inaudible commands, and replay attacks.Source: A. Cybersecurity Attacks and… · p. 12

B. Cybersecurity Risk and Vulnerabilities: ChatGPT

• Sensitive internal data, source code, and client data were among the most common confidential information submitted to ChatGPT, with reported counts of 319, 278, and 260, respectively.Source: B. Cybersecurity Risk and… · p. 12
• A ChatGPT outage exposed personal information to other users, and OpenAI reported that 1.2% of ChatGPT Plus users might have had personal data revealed.Source: B. Cybersecurity Risk and… · p. 12
• ChatGPT-related risks include data exposure, fake websites harvesting payment information, malware disguised as ChatGPT, billing fraud, and a malicious extension downloaded by at least 2,000 people.Source: B. Cybersecurity Risk and… · p. 12

A. Challenges

• Modern cyberattacks have become more sophisticated and stealthier, making traditional protections inadequate for current cybersecurity risks.Source: A. Challenges · p. 12
• ChatGPT can spread misinformation and support malicious activities including malicious code, scam giveaways, zero-day attacks, fake landing pages, phishing emails, and adversary-in-the-middle attacks.Source: A. Challenges · p. 13
• Jailbreaking lets bad actors bypass ChatGPT’s security measures and obtain malicious content.Source: A. Challenges · p. 13
• Generative AI can produce difficult-to-recognize deepfake text that spreads misinformation and propaganda and may affect reputations.Source: A. Challenges · p. 13
• ChatGPT-generated phishing emails can remove grammar-based warning signs, lower barriers for attackers, and make cybercrime attempts harder to trace.Source: A. Challenges · p. 13
• ChatGPT-generated text raises plagiarism and authorship concerns, while organizations warn employees against sharing confidential code because outputs may resemble it.Source: A. Challenges · p. 13
• Inexpert users may deploy poorly designed models that create discrimination, bias, and safety risks, while ChatGPT hallucinations can produce seemingly reasonable but incorrect answers.Source: A. Challenges · p. 13

B. Future Directions

• End-to-end encryption protects chatbot conversations from third-party access and supports personal-data protection requirements.Source: B. Future Directions · p. 13
• Authentication and authorization should verify identities, control access, and include strict filtering to detect sensitive content.Source: B. Future Directions · p. 13
• Static and dynamic analysis can assess chatbot permissions and privacy policies; in Discord, only 4.35% of chatbots provided consent with a privacy policy.Source: B. Future Directions · p. 13
• Robust detection tools, user instruction, transparency, supervision, monitoring, and filtering are recommended to address realistic AI-generated text and unethical use.Source: B. Future Directions · p. 13
• Users should be educated not to copy and paste confidential information into generative AI tools, despite OpenAI’s statement about submitted data.Source: B. Future Directions · p. 14
• Chatbot security requires continuous modern practices, policies, initial assessments, and regular updates because chatbots introduce ongoing cybersecurity risks.Source: B. Future Directions · p. 14

VI. CONCLUSION

• The conclusion reports that ChatGPT can create malicious code, phishing emails, undetectable zero-day attacks, macros, and LOLBINs, while chatbot vulnerabilities have enabled notable incidents.Source: §VI · p. 14