Neutralizing Structural Vulnerabilities in Token-Oriented Object Notation (TOON): The S-TOON Protocol for Secure Outputs

The rapid optimization of Generative AI inference has attracted attention from rigid serialization standards (JSON) to the suggested Token-Oriented Object Notation (TOON), yielding payload reductions of 30-50%. However, this removal of explicit syntax introduces a critical architectural regression: "Delimiter Dissolution". This foundational flaw exposes the architecture to a spectrum of distinct structural vulnerabilities (including Type Smuggling and Economic DoS), where the boundaries between untrusted user input and trusted system schemas collapse due to semantic ambiguity. This paper analyzes this "Serialization-Security Trade-off," demonstrating that standard TOON implementations exhibit a 100% susceptibility to the full spectrum of structural exploits on both Edge (TinyLlama) and Cloud (Qwen-2.5) architectures. To reconcile efficiency with security, we introduce S-TOON (Strict-TOON), a neuro-symbolic security protocol enforced via a middleware architecture. The system makes two primary contributions: (1) Deterministic Sentinel Injection, which enforces a "Virtual Faraday Cage" around untrusted input to prevent parser desynchronization, and (2) Adaptive Chain-of-Thought (CoT) Enforcement, which overrides the semantic bias of high-parameter models. Experimental validation confirms that S-TOON imposes a deterministic structural boundary, resulting in a 0.0% Attack Success Rate (ASR) across the entire 220,000-shot validation suite. Unlike probabilistic defenses, the middleware deterministically neutralizes structural ambiguity via middleware enforcement, effectively eliminating the existence of the attack vector within the context window across all tested model classes while retaining 95% of the token efficiency required for high-throughput environments.