Symbolic Logic Validation of LLM Interactions in Critical Systems

Abstract

Large language models (LLMs) have demonstrated significant capabilities in natural language understanding and decision support. However, their deployment in critical environments requires robust mechanisms to ensure dependability and prevent erroneous inputs or outputs from compromising system dependability. This paper presents a symbolic logic reasoning approach for enhancing the validation of both user inputs and LLM outputs in critical systems. We propose a validation framework that combines the flexibility of LLMs with the logic reasoning capabilities of Answer Set Programming (ASP) as a complementary layer to existing guardrail mechanisms. The approach enables systematic validation through declarative patterns for fault tolerance and logic reasoning. Our method validates user prompts and LLM responses against external knowledge bases and domain-specific constraints using multiple strategies, including redundancy-based validation, knowledge base auditing, and consistency checking. By transforming natural language interactions into logical representations, the framework leverages ASP’s non-monotonic reasoning to detect logical inconsistencies, contradictions, knowledge reproduction errors, and constraint violations throughout the interaction pipeline. The system provides both binary decision outputs (Go/NoGo) and detailed feedback about the reasoning artifacts for auditability and explainability. We demonstrate the practical implementation through a modular architecture that supports customizable validation components.