Conversation Reconstruction Attack Against GPT Models

Advancements in GPT models pose privacy risks in multi-round conversations, requiring attention.

Summary:

• The article discusses the privacy risks associated with multi-round conversations with GPT models, introducing the Conversation Reconstruction Attack and evaluating the vulnerability of GPT models to advanced attacks. It also presents defense strategies to mitigate privacy risks.

Major Findings:

1. GPT models are vulnerable to privacy leakage and advanced attacks, with GPT-4 demonstrating some resilience.
2. The vulnerability of GPT models to privacy attacks is influenced by different task types, character types, and numbers of chat rounds.
3. The article introduces defense strategies to counter privacy attacks and mitigate privacy risks in conversations with GPT models.

Analysis and Critique:

• The article provides valuable insights into the privacy risks associated with GPT models and the potential misuse of these models in multi-round conversations. It highlights the need for robust defense strategies to mitigate privacy risks and emphasizes the importance of addressing privacy concerns in conversations with GPT models.
• The findings underscore the significance of developing privacy protection mechanisms to prevent unauthorized access to sensitive information and protect user privacy in AI applications.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	https://arxiv.org/abs/2402.02987v1
HTML	https://browse.arxiv.org/html/2402.02987v1
Truncated	True
Word Count	19717