Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures

As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs. The analysis shows that inputs are often interpreted non-neutrally and can trigger implementation-shaped responses or unintended state changes even without explicit commands. We argue that these behaviors constitute architectural failure modes and that string-level filtering alone is insufficient. To mitigate such cross-stage vulnerabilities, we recommend zero-trust architectural principles, including provenance enforcement, context sealing, and plan revalidation, and we introduce "Countermind" as a conceptual blueprint for implementing these defenses.

Key Contributions

Mechanism-centered taxonomy of 41 recurring risk patterns arising from unvalidated trust in multi-stage commercial LLM pipelines
Empirical analysis demonstrating that inputs can trigger implementation-shaped responses and unintended state changes without explicit commands, establishing these as architectural failure modes
Countermind — a conceptual zero-trust defense blueprint incorporating provenance enforcement, context sealing, and plan revalidation for LLM pipeline security