Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models
Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable systems, full per-configuration evaluation and optimization is impractical. In this paper, we formalize the behavioral geometry of a population of models that, by leveraging previously evaluated and defended models, supports both efficient susceptibility prediction and effective defense transfer across a population. We apply the framework to 79 models spanning 24 providers and to 100 system configurations of a single base model. Simple methods that use the behavioral geometry reach an AUPRC of $0.94$ for susceptibility detection with $\appr...
| Authors | Hayden Helm, Xiaodong Liu, Weiwei Yang |
|---|---|
| Primary category | cs.CR |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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