Security Analysis of ChatGPT: Threats and Privacy Risks

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.

💡 Research Summary

The paper provides a comprehensive security and privacy assessment of ChatGPT, the widely deployed large‑language model (LLM) from OpenAI. It begins with a historical overview of artificial‑intelligence development, emphasizing the pivotal role of the Transformer architecture introduced in 2017 and tracing ChatGPT’s evolution through four major versions. Version 1 started with a modest decoder‑only network, Version 2 expanded depth and parameters, Version 3 reached 175 billion parameters and demonstrated strong zero‑shot capabilities, while Version 4 further improved reasoning, stability, and exam‑level performance. The authors then detail the internal mechanisms of the Transformer—word embeddings, positional encodings, encoder‑decoder stacks, and multi‑head self‑attention—explaining how these components enable high‑quality text generation but also create attack surfaces.

In the security‑threat section, the paper categorizes traditional social‑engineering attacks (phishing, smishing) that are now automated by ChatGPT, enabling attackers to craft persuasive malicious content at scale. It introduces prompt‑injection attacks, where adversarial instructions are hidden within user prompts to manipulate model outputs, bypassing content filters. Model‑stealing and reverse‑engineering techniques are examined, showing how API responses can be used to infer model parameters and reconstruct training data, raising concerns about intellectual‑property theft and privacy leakage. The authors also discuss the generation of malicious code, disinformation, and the potential for LLMs to be weaponized in cyber‑espionage.

The privacy‑risk analysis highlights training‑data leakage, where the model may regurgitate personally identifiable information (PII) present in its massive corpora. Experiments demonstrate that targeted queries can elicit sensitive details, confirming the risk of inadvertent data exposure. Data‑poisoning attacks are explored, illustrating how adversaries can inject biased or harmful samples into the training set to steer model behavior, thereby compromising fairness and safety.

To validate these threats, the authors conduct simulated attacker experiments: automated phishing‑email generation, malicious script alteration, and response manipulation. From a defender’s perspective, they experiment with using ChatGPT itself as a security assistant—automating vulnerability scanning, generating security policies, and assisting log analysis. Results show that LLM‑based tools can accelerate detection and policy drafting, yet they also inherit the model’s false‑positive and hallucination problems.

The paper concludes with a multi‑layered mitigation roadmap: enforce strict API access controls and usage monitoring; implement robust prompt sanitization and input validation; apply differential privacy and data minimization during model training; continuously monitor model outputs for anomalous behavior; and integrate human expert oversight when deploying LLM‑driven security solutions. By treating ChatGPT simultaneously as a potential attack vector and a defensive aid, the authors underscore the need for balanced governance, ongoing research, and adaptive security practices.