Inference should be treated as a chained trust negotiation, not a flat prompt string.
Research Paper
The Solution to Prompt Injection
A Structural Approach to LLM Security
The Solution to Prompt Injection
A Structural Approach to LLM Security
Abstract
This paper outlines a structural model for mitigating prompt injection by mapping SSL/TLS trust architecture onto LLM inference. It proposes a layered trust boundary, policy normalization, and deterministic routing that preserve model utility while hardening instruction pathways.
Key Findings
Clear trust tiers prevent untrusted content from overriding system-level intent.
Policy normalization creates a stable substrate for safe tool and data access.
Audit-ready routing reduces ambiguity in LLM decision boundaries.
How to Cite
Griggs, Jay. "The Solution to Prompt Injection: A Structural Approach to LLM Security." February 2026. SolvingPromptInjection.com.