Remote Voting Security Under Adversarial AI: Requirement Promotion, a Taxonomy, and a Reference Architecture

Abstract

The classical taxonomy of remote voting security requirements is organized into two levels: basic and counter-attack requirements. This classification has remained largely unchanged over a decade, even through scheme innovations such as verifiable re-voting, tally-hiding, and post-quantum protocols. However, this stability does not survive the new threat model of adversarial artificial intelligence (AI). A security requirement is considered promoted when AI raises the adversarial grade at which it must be defended above its original assumption. In this paper, we introduce requirement promotion as a framework for re-evaluating the classical taxonomy, contending that the destabilizing factor is not new cryptography but the emergence of a new adversary. We analyze five requirements under both AI-amplified threats and AI-enabled defenses, demonstrating that promotion fires selectively. Basic requirements such as privacy, fairness, and eligibility undergo tier promotion to counter-attack grade, driven by machine-learning-based deanonymization, pre-tally outcome inference, and synthetic-identity fraud. Incoercibility undergoes supra-tier promotion, surpassing the existing counter-attack toolkit, as deepfake-generated coercion evidence compromises the fake-credential assumptions of classical coercion-resistant schemes. Verifiability, where AI-based defense is robust, resists headline promotion but acquires a new sub-requirement at its seam with software independence: verifying the opaque machine-learning components in the audit pipeline itself. We then propose a reference architecture, integrating existing primitives such as lattice-based zero-knowledge proofs, deniable re-voting, statistical election forensics, and time-lock decryption into a layered design that addresses the promoted requirements, with explicit analysis of residual gaps.