This research reveals how safety classifiers embedded during alignment can be extracted and analyzed to improve security against jailbreak attacks in large language models.

• Introduces a novel method to extract surrogate safety classifiers from aligned LLMs
• Demonstrates how these extracted classifiers provide insights into LLM vulnerability patterns
• Helps identify weaknesses in current safety mechanisms that attackers might exploit
• Provides a practical approach to evaluate and improve LLM robustness against malicious inputs

For security professionals, this research offers valuable tools to assess alignment quality and safeguard LLMs against emerging threats by understanding the internal safety mechanisms that prevent harmful outputs.

Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs