Machine Learning for Detection and Analysis of Novel LLM Jailbreaks
This addresses security vulnerabilities in LLMs for developers and users, but it is incremental as it builds on existing methods and datasets.
The study tackled the problem of detecting jailbreak prompts that circumvent safety guardrails in Large Language Models, finding that fine-tuning a BERT model achieved the best performance on current datasets.
Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-called jailbreak prompts are designed to trick the LLM into circumventing the safety guardrails put in place to keep responses acceptable to the developer's policies. In this study, we analyse the ability of different machine learning models to distinguish jailbreak prompts from genuine uses, including looking at our ability to identify jailbreaks that use previously unseen strategies. Our results indicate that using current datasets the best performance is achieved by fine tuning a Bidirectional Encoder Representations from Transformers (BERT) model end-to-end for identifying jailbreaks. We visualise the keywords that distinguish jailbreak from genuine prompts and conclude that explicit reflexivity in prompt structure could be a signal of jailbreak intention.