Authors: Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang

Published on: February 09, 2024

Impact Score: 8.05

Arxiv code: Arxiv:2402.06255

Summary

• What is new: This paper introduces Prompt Adversarial Tuning (PAT) as a novel defense strategy for Large Language Models to prevent them from producing harmful content, marking a new perspective by focusing on prompt tuning for defense.
• Why this is important: Large Language Models are vulnerable to prompts that can make them generate dangerous or illegal content, known as the jailbreak phenomenon.
• What the research proposes: The proposed solution, Prompt Adversarial Tuning (PAT), trains a defense mechanism that is attached as a prefix to user prompts to safeguard the models without affecting their operational efficiency.
• Results: Experiments demonstrate that PAT significantly lowers the success rate of advanced attacks to nearly 0%, while maintaining an 80% response accuracy to benign queries in both black-box and white-box settings.

Technical Details

Technological frameworks used: Prompt Adversarial Tuning (PAT)

Models used: Large Language Models

Data used: Adversarial and benign prompts

Potential Impact

This research could benefit cybersecurity firms, tech companies developing or implementing Large Language Models, and organizations focusing on digital safety and content moderation.

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