Decoupling Evidence from Execution: A Zero-Knowledge Runtime Authority and Dynamic Refusal Protocol for Agentic AI

The rapid paradigm shift from passive, advisory Large Language Models (LLMs) to autonomous, agentic artificial intelligence systems has introduced critical execution risks. Traditional AI governance frameworks operate predominantly at the "evidence" layer-documenting data provenance, recording audit trails, and logging static safety evaluations. However, a structural vulnerability arises during the downstream execution phase: under operational pressure, autonomous agents can experience "authority drift," executing high-consequence actions based on stale dependencies, bypassed safety states, or invalid runtime authorities. To resolve this decoupling paradox, this paper introduces the Zero-Knowledge Kill-Switch (ZKKS), a cryptographic runtime enforcement architecture designed for Zero-Knowledge Web Servers (ZKWS). Rather than relying on post-hoc logging, ZKKS acts as a network-level, math-enforced execution barrier. By compiling safety policies into non-interactive zero-knowledge proofs (zk-SNARKs) and enforcing them via a Linear Temporal Logic (LTL) runtime state machine, the ZKWS dynamically halts downstream actions at the point of execution when a mathematical invariant or freshness threshold is violated-without decrypting or accessing the underlying private data payloads. We prove that ZKKS bounds operational failure to zero under deterministic policy constraints, bridging the critical gap between upstream integrity evidence and downstream execution control.

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