When attacking Large Language Models (LLMs), are manual or automated attacks more effective? We set out to answer this question in our latest research paper, analyzing data from Dreadnode’s Crucible platform and observing patterns in LLM attack execution methods.
We found that automated approaches achieve significantly higher success rates (69.5%) compared to manual techniques (47.6%) when leveraged against the AI Red teaming challenges in Crucible. However, only 5.2% of users are employing automation.
We found that automated approaches achieve significantly higher success rates (69.5%) compared to manual techniques (47.6%) when leveraged against the AI Red teaming challenges in Crucible. However, only 5.2% of users are employing automation.