An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
This work provides a principled and interpretable framework for evaluating jailbreak attacks, which is important for researchers and practitioners in AI safety, though it is incremental in improving evaluation methods.
The authors tackled the problem of comparing jailbreaking attacks on large language models by proposing a unified threat model based on an N-gram language model, finding that attack success rates are lower than previously reported and that discrete optimization attacks outperform LLM-based attacks.
A plethora of jailbreaking attacks have been proposed to obtain harmful responses from safety-tuned LLMs. These methods largely succeed in coercing the target output in their original settings, but their attacks vary substantially in fluency and computational effort. In this work, we propose a unified threat model for the principled comparison of these methods. Our threat model checks if a given jailbreak is likely to occur in the distribution of text. For this, we build an N-gram language model on 1T tokens, which, unlike model-based perplexity, allows for an LLM-agnostic, nonparametric, and inherently interpretable evaluation. We adapt popular attacks to this threat model, and, for the first time, benchmark these attacks on equal footing with it. After an extensive comparison, we find attack success rates against safety-tuned modern models to be lower than previously presented and that attacks based on discrete optimization significantly outperform recent LLM-based attacks. Being inherently interpretable, our threat model allows for a comprehensive analysis and comparison of jailbreak attacks. We find that effective attacks exploit and abuse infrequent bigrams, either selecting the ones absent from real-world text or rare ones, e.g., specific to Reddit or code datasets.