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

Dominik Schwarz

TL;DR

This work tackles semantic security in multi-stage LLM pipelines by revealing cross-stage vulnerabilities that arise when downstream components implicitly trust upstream outputs. It introduces a mechanism-centered taxonomy of 41 risk patterns, backed by empirical observations across provider-default, text-only sessions with three commercial models and a local offline baseline, using metrics DS, IEO, POB, PDI, and RR. The study identifies core architectural failure modes—unvalidated trust inheritance, interpretation-driven state changes, and cross-modal/structural influences—arguing that traditional string filtering is insufficient. It proposes zero-trust architectural principles, including provenance enforcement, context sealing, and plan revalidation, and outlines Countermind as a companion blueprint for implementing these defenses. Collectively, the findings support defense-in-depth strategies to curb cross-stage manipulation, offering concrete guidance for auditing, architecture design, and ongoing evaluation in real-world AI pipelines.

Abstract

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.

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

TL;DR

This work tackles semantic security in multi-stage LLM pipelines by revealing cross-stage vulnerabilities that arise when downstream components implicitly trust upstream outputs. It introduces a mechanism-centered taxonomy of 41 risk patterns, backed by empirical observations across provider-default, text-only sessions with three commercial models and a local offline baseline, using metrics DS, IEO, POB, PDI, and RR. The study identifies core architectural failure modes—unvalidated trust inheritance, interpretation-driven state changes, and cross-modal/structural influences—arguing that traditional string filtering is insufficient. It proposes zero-trust architectural principles, including provenance enforcement, context sealing, and plan revalidation, and outlines Countermind as a companion blueprint for implementing these defenses. Collectively, the findings support defense-in-depth strategies to curb cross-stage manipulation, offering concrete guidance for auditing, architecture design, and ongoing evaluation in real-world AI pipelines.

Abstract

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.

Paper Structure

This paper contains 806 sections, 8 equations, 4 figures, 102 tables.

Table of Contents

  1. Introduction
  2. Motivation and Problem Statement
  3. Contributions of this Paper
  4. Mechanism-centered taxonomy and empirical mapping.
  5. Cross-sectional architectural findings.
  6. Capability and safety scaling pattern.
  7. Implications for defense.
  8. Background
  9. Generative Models, Agents, and Tool/Connector Ecosystems
  10. Generative models and Large Language Models.
  11. Transformer architecture.
  12. From Large Language Models to agent frameworks.
  13. Tool and connector ecosystems.
  14. Multimodality and Context Windows
  15. Multimodal pipelines.
  16. ...and 791 more sections

Figures (4)

  • Figure 1: A diagram illustrating the Attack Graph, depicting the relationships and potential escalation pathways among the different risk patterns classes as detailed in Section \ref{['sec:attack_graph']}
  • Figure 2: The diagram includes layers like the Multimodal Sandbox, Intent Routing, and the Core Large Language Model with Introspective Filters, with callouts indicating which threat classes are mitigated at each layer.
  • Figure 3: OCR Example 1
  • Figure 4: Visual Trigger