Security Analysis of ChatGPT: Threats and Privacy Risks
- URL: http://arxiv.org/abs/2508.09426v1
- Date: Wed, 13 Aug 2025 02:03:18 GMT
- Title: Security Analysis of ChatGPT: Threats and Privacy Risks
- Authors: Yushan Xiang, Zhongwen Li, Xiaoqi Li,
- Abstract summary: The security of ChatGPT is mainly studied from two aspects, security threats and privacy risks.<n>Briefly, we discuss the controversies that ChatGPT may cause at the ethical and moral levels.<n>This paper reproduces several network attack and defense test scenarios by simulating the attacker's perspective and methodology.
- Score: 1.8876605885609514
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As artificial intelligence technology continues to advance, chatbots are becoming increasingly powerful. Among them, ChatGPT, launched by OpenAI, has garnered widespread attention globally due to its powerful natural language processing capabilities based on the GPT model, which enables it to engage in natural conversations with users, understand various forms of linguistic expressions, and generate useful information and suggestions. However, as its application scope expands, user demand grows, and malicious attacks related to it become increasingly frequent, the security threats and privacy risks faced by ChatGPT are gradually coming to the forefront. In this paper, the security of ChatGPT is mainly studied from two aspects, security threats and privacy risks. The article systematically analyzes various types of vulnerabilities involved in the above two types of problems and their causes. Briefly, we discuss the controversies that ChatGPT may cause at the ethical and moral levels. In addition, this paper reproduces several network attack and defense test scenarios by simulating the attacker's perspective and methodology. Simultaneously, it explores the feasibility of using ChatGPT for security vulnerability detection and security tool generation from the defender's perspective.
Related papers
- An Empirical Study on the Security Vulnerabilities of GPTs [48.12756684275687]
GPTs are one kind of customized AI agents based on OpenAI's large language models.<n>We present an empirical study on the security vulnerabilities of GPTs.
arXiv Detail & Related papers (2025-11-28T13:30:25Z) - Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial [107.26005706569498]
Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics.<n>Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions.
arXiv Detail & Related papers (2025-08-04T01:42:57Z) - Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context [45.821481786228226]
We show that situation-driven adversarial full-prompts that leverage situational context are effective but much harder to detect.<n>We developed attacks that use movie scripts as situational contextual frameworks.<n>We enhanced the AdvPrompter framework with p-nucleus sampling to generate diverse human-readable adversarial texts.
arXiv Detail & Related papers (2024-12-20T21:43:52Z) - A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality [1.7624347338410744]
ChatGPT is a Large Language Model (LLM) that can perform a variety of tasks with remarkable semantic understanding and accuracy.
This study aims to gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security.
It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing.
arXiv Detail & Related papers (2024-08-01T10:14:05Z) - Unveiling Security, Privacy, and Ethical Concerns of ChatGPT [6.588022305382666]
ChatGPT uses topic modeling and reinforcement learning to generate natural responses.
ChatGPT holds immense promise across various industries, such as customer service, education, mental health treatment, personal productivity, and content creation.
This paper focuses on security, privacy, and ethics issues, calling for concerted efforts to ensure the development of secure and ethically sound large language models.
arXiv Detail & Related papers (2023-07-26T13:45:18Z) - How Does Naming Affect LLMs on Code Analysis Tasks? [8.150719423943109]
Large Language Models (LLMs) were proposed for natural language processing (NLP) and have shown promising results as general-purpose language models.
This paper investigates how naming affects LLMs on code analysis tasks.
arXiv Detail & Related papers (2023-07-24T02:38:24Z) - From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and
Privacy [0.0]
This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy.
The paper investigates how cyber offenders can use the GenAI tools in developing cyber attacks.
We will also discuss the social, legal, and ethical implications of ChatGPT.
arXiv Detail & Related papers (2023-07-03T00:36:57Z) - Chatbots to ChatGPT in a Cybersecurity Space: Evolution,
Vulnerabilities, Attacks, Challenges, and Future Recommendations [6.1194122931444035]
OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch.
With the enhanced popularity, ChatGPT experienced cybersecurity threats and vulnerabilities.
arXiv Detail & Related papers (2023-05-29T12:26:44Z) - To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection.
We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains.
Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
arXiv Detail & Related papers (2023-04-04T03:04:28Z) - Consistency Analysis of ChatGPT [65.268245109828]
This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour.
Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions.
arXiv Detail & Related papers (2023-03-11T01:19:01Z) - A Categorical Archive of ChatGPT Failures [47.64219291655723]
ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation.
It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries.
However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study.
arXiv Detail & Related papers (2023-02-06T04:21:59Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.