A Realistic Threat Model for Large Language Model Jailbreaks

Valentyn Boreiko, Alexander Panfilov, Vaclav Voracek, Matthias Hein, Jonas Geiping

Keywords: LLM, jailbreaks, threat model, robustness

TL;DR: Jailbreaking attacks are not comparable - we propose a way to do so via a realistic threat model and show, how to adapt popular attacks to it.

Abstract:

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 combines constraints in perplexity, measuring how far a jailbreak deviates from natural text and computational budget in total FLOPs.
For the former, we build an N-gram language model on 1T tokens, which, unlike model-based perplexity, allows for an LLM-agnostic and inherently interpretable evaluation. We adapt popular attacks to this new, realistic threat model, with which we, for the first time, benchmark these attacks on equal footing. After a rigorous 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 N-grams, either selecting N-grams absent from real-world text or rare ones, e.g., specific to code datasets.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4009