ReCast: Contract-Preserving Protection for Fixed-Interface Multimodal Reasoning

Abstract:Remote multimodal models offer strong numerical reasoning capabilities over charts and speech, but sending private inputs risks exposing sensitive content. Text-only sanitization cannot directly satisfy fixed media interfaces, while identity anonymization leaves the underlying task content exposed. We introduce ReCast, an agentic plug-in framework that replaces source-specific content while preserving task-relevant relations and the required input modality. ReCast locally converts inputs into a shared textual evidence-query record, jointly rewrites entities and topics with a distilled 4B model, and substitutes values through a locally invertible, role-aware numerical map. A reconstruction agent generates and validates the required media from the protected record. The remote solver returns a program whose protected operands are restored locally before execution. On 4,000 held-out ChartQA and NMSQA examples, ReCast achieves 75.10% accuracy, retaining 92.43% of unprotected remote accuracy, while a model-based audit flags source-content leakage in 7.95% of solver-bound requests. It outperforms all evaluated local baselines, preserving the benefit of remote reasoning while reducing source-content exposure under existing media interfaces.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.01184 [cs.CR]

(or arXiv:2610.01184v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2610.01184 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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