survey 2025

Security Analysis of ChatGPT: Threats and Privacy Risks

Yushan Xiang , Zhongwen Li , Xiaoqi Li

0 citations

α

Published on arXiv

2508.09426

Model Theft

OWASP ML Top 10 — ML05

Model Inversion Attack

OWASP ML Top 10 — ML03

Prompt Injection

OWASP LLM Top 10 — LLM01

Sensitive Information Disclosure

OWASP LLM Top 10 — LLM06

Key Finding

ChatGPT faces concrete, reproducible security threats across prompt injection, training data leakage, and model theft, while also showing potential as a defender for security vulnerability detection.


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.


Key Contributions

  • Systematic categorization of security threats and privacy risks specific to ChatGPT/LLMs, including malicious content generation, prompt injection, data leakage, and model theft
  • Reproduction of simulated attack-and-defense scenarios validating feasibility of identified attack modes
  • Exploration of using ChatGPT as a tool for security vulnerability detection and defensive tool generation

🛡️ Threat Analysis

Model Inversion Attack

Paper covers training data leakage and information reconstruction as key privacy risks — an adversary recovers private training data from model outputs.

Model Theft

Paper explicitly identifies model stealing/reverse engineering as a primary threat category against ChatGPT, covering how adversaries clone or reconstruct the model.


Details

Domains
nlp
Model Types
llmtransformer
Threat Tags
black_boxinference_timetraining_time
Applications
conversational aichatbotslarge language models