Our team is feeling overwhelmed trying to retrain our prompt injection classifiers each time a new jailbreak is released. At first, we were retraining monthly, then it dropped to weekly, and now we're seeing almost daily updates due to the creative ways people are bypassing our models. The evaluation pipeline itself takes about 6 hours, and then there's the whole process of data labeling, hyperparameter tuning, and testing deployments.
Has anyone found a more effective way to handle this? We've experimented with ensemble methods and rule-based fallbacks, but keep encountering coverage gaps. I'm considering a switch to more dynamic detection methods, although I'm concerned about potential latency issues.
2 Answers
It sounds like you're in a tough spot! One thing you might want to consider is implementing continuous learning frameworks that can adapt to new data as it comes in without the need for full retraining. This can save you a lot of time and reduce the latency issues you're worried about. Have you looked into more automated pipelines that can handle data labeling and tuning efficiently?
Switching to dynamic detection could definitely help, but you might want to focus on monitoring and adapting on the fly. Consider integrating feedback loops from your users to constantly refine your approach and address those coverage gaps. It’s more of a proactive strategy than reacting to every jailbreak. Plus, pairing that with some anomaly detection could help flag new bypasses earlier!
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