When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models

1. 1Identify potential vulnerabilities in LLMs using fixed collections of harmful prompts
2. 2Develop adaptive red-teaming methods to simulate iterative prompt optimization attacks
3. 3Evaluate LLMs' robustness against these adaptive attacks
4. 4Refine LLMs' safety guarantees based on the results of the adaptive red-teaming

This research benefits AI engineers and ML researchers working on LLMs, as it highlights the importance of robust safety evaluations and provides a new approach to testing LLMs against adaptive attacks

💡 Adaptive red-teaming can help identify and mitigate potential vulnerabilities in LLMs by simulating realistic attack scenarios

🚨 Adaptive red-teaming for LLMs: a new approach to testing safety against iterative prompt optimization attacks 🚨