This research introduces a dramatically more efficient method for breaking through LLM safety measures using Projected Gradient Descent (PGD) on continuously relaxed input prompts.

• Reduces computational requirements from 100,000+ LLM calls to just ~100 calls
• Achieves comparable effectiveness to discrete optimization methods while being 1000x more efficient
• Enables new applications like quantitative vulnerability analysis and adversarial training
• Demonstrates serious security implications for current LLM alignment methods

For security professionals, this work highlights critical vulnerabilities in existing LLM safety mechanisms while providing a more practical framework for testing and improving model defenses against adversarial attacks.

Attacking Large Language Models with Projected Gradient Descent