SPARK: Representation-Level KV Memory Alignment for Safer Vision-Language Models
Vision-language models (VLMs) remain vulnerable to jailbreaks that distribute harmful intent across text and images, making unimodal safety mechanisms insufficient. We investigate whether this vulnerability can be mitigated directly in the multimodal key-value (KV) memory formed during prefill, without modifying model parameters at inference time. We introduce SPARK, a two-stage framework for targeted KV-memory repair. Stage 1 uses a disposable diagnostic adapter to identify harm-associated directions in multimodal key and value representations. Stage 2 projects out these directions, learns a lightweight residual repair, and anchors repaired keys with an image-structural prior to preserve...
| Authors | Mohd Azfar, Izhar Dad Khan |
|---|---|
| Primary category | cs.CV |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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