Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Models
On-device Vision-Language Models (VLMs) promise data privacy via local execution. However, we show that the architectural shift toward Dynamic High-Resolution preprocessing (e.g., AnyRes) introduces an inherent algorithmic side-channel. Unlike static models, dynamic preprocessing decomposes images into a variable number of patches based on their aspect ratio, creating workload-dependent inputs. We demonstrate a dual-layer attack framework against local VLMs. In Tier 1, an unprivileged attacker can exploit significant execution-time variations using standard unprivileged OS metrics to reliably fingerprint the input's geometry. In Tier 2, by profiling Last-Level Cache (LLC) contention, the ...
| Authors | Eyal Hadad, Mordechai Guri |
|---|---|
| Primary category | cs.CR |
| Published | 26 March 2026 |
| Last updated at source | 27 March 2026 |
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