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Assessing Prompt Injection Risks in 200+ Custom GPTs

Authors: Jiahao Yu, Yuhang Wu, Dong Shu, Mingyu Jin, Sabrina Yang, Xinyu Xing

Published: 2023-11-20

arXiv ID: 2311.11538v2

Added to Library: 2025-11-11 14:02 UTC

Red Teaming

📄 Abstract

In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications. The new feature - customization of ChatGPT models by users to cater to specific needs has opened new frontiers in AI utility. However, this study reveals a significant security vulnerability inherent in these user-customized GPTs: prompt injection attacks. Through comprehensive testing of over 200 user-designed GPT models via adversarial prompts, we demonstrate that these systems are susceptible to prompt injections. Through prompt injection, an adversary can not only extract the customized system prompts but also access the uploaded files. This paper provides a first-hand analysis of the prompt injection, alongside the evaluation of the possible mitigation of such attacks. Our findings underscore the urgent need for robust security frameworks in the design and deployment of customizable GPT models. The intent of this paper is to raise awareness and prompt action in the AI community, ensuring that the benefits of GPT customization do not come at the cost of compromised security and privacy.

🔍 Key Points

  • Identified significant vulnerabilities in over 200 customizable GPT models, specifically related to prompt injection attacks that allow adversaries to extract sensitive system prompts and uploaded files.
  • Developed a systematic method for conducting prompt injection attacks that includes scanning custom GPTs, injecting adversarial prompts, and extracting sensitive data, demonstrating high success rates in such attacks.
  • Conducted red-teaming evaluations on defense mechanisms against prompt injection, revealing that current defenses are insufficient and can be easily bypassed when targeting custom GPTs with code interpreters.
  • Proposed the urgent need for enhanced security frameworks to secure custom GPTs in the face of user-generated content and potential malicious exploitation.
  • Highlighted the balance between innovation in customizable AI and the need for robust security measures, urging the AI community to prioritize security as they leverage these advanced tools.

💡 Why This Paper Matters

This paper is critical as it sheds light on the security vulnerabilities associated with customizable GPT models, an increasingly popular feature within AI technologies. By identifying profound weaknesses that allow for serious exploits via prompt injection, the research underscores an urgent call for improved security strategies within the AI community. The findings highlight that while customizing AI for diverse applications offers significant benefits, these innovations must not compromise the security and privacy of users and their data.

🎯 Why It's Interesting for AI Security Researchers

This paper is of great interest to AI security researchers as it addresses a burgeoning security threat in AI systems. Prompt injection attacks represent an evolving challenge in the field of natural language processing, showcasing how adversaries can leverage AI's architecture against itself. The novel methodologies introduced for evaluating and exploiting vulnerabilities present new avenues for research into defensive mechanisms, making this work essential for anyone focused on enhancing the security of AI applications.

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