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Learning diverse attacks on large language models for robust red-teaming and safety tuning

Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang · May 28, 2024

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 30% Moderate protocol signal Freshness: Cold Status: Fallback
Red Team General
  • We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs.
  • Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
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