Prompt Optimization and Evaluation for LLM Automated Red Teaming

• URL: http://arxiv.org/abs/2507.22133v1
• Date: Tue, 29 Jul 2025 18:08:01 GMT
• Title: Prompt Optimization and Evaluation for LLM Automated Red Teaming
• Authors: Michael Freenor, Lauren Alvarez, Milton Leal, Lily Smith, Joel Garrett, Yelyzaveta Husieva, Madeline Woodruff, Ryan Miller, Erich Kummerfeld, Rafael Medeiros, Sander Schulhoff,
• Abstract summary: We introduce a method for optimizing attack generator prompts that applies ASR to individual attacks.<n>By repeating each attack against a randomly seeded target, we measure an attack's discoverability the expectation of the individual attack success.
• Score: 0.5259346146529064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applications that use Large Language Models (LLMs) are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluated using the Attack Success Rate (ASR) the sample mean calculated over the judgment of success for each attack. In this paper, we introduce a method for optimizing attack generator prompts that applies ASR to individual attacks. By repeating each attack multiple times against a randomly seeded target, we measure an attack's discoverability the expectation of the individual attack success. This approach reveals exploitable patterns that inform prompt optimization, ultimately enabling more robust evaluation and refinement of generators.

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