Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization
Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual information in a shared embedding space. Yet, this flexibility also makes them vulnerable to malicious prompts designed to produce unsafe content, raising critical safety concerns. Existing defenses either rely on blacklist filters, which are easily circumvented, or on heavy classifier-based systems, both of which are costly and fragile under embedding-level attacks. We address these challenges with two complementary components: Hyperbolic Prompt Espial (HyPE) and Hyperbolic Prompt Sanitization (HyPS). HyPE is a lightweight anomaly detector that l...
| Authors | Igor Maljkovic, Maria Rosaria Briglia, Iacopo Masi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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