Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures (2510.27190v1)

Abstract: As 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.

• The paper introduces a mechanism-centered taxonomy identifying 41 risk patterns and cross-stage vulnerabilities in LLM architectures.
• It empirically evaluates failure modes using metrics like IEO and POB, revealing significant capacity–safety scaling mismatches.
• The proposed countermeasures enforce provenance tagging and mandatory revalidation to mitigate privilege escalation and state-based attacks.

Cross-Stage Semantic Vulnerabilities in LLM Architectures: Mechanisms, Empirical Mapping, and Architectural Countermeasures

Introduction

The integration of LLMs into increasingly complex, multimodal, tool-enabled, and agentic architectures has introduced advanced security risks that surpass traditional input-output filtering paradigms. The paper “Unvalidated Trust: Cross-Stage Vulnerabilities in LLM Architectures” (2510.27190) presents a comprehensive empirical and architectural analysis of vulnerabilities arising from systemic trust inheritance, latent intent propagation, and interpretation-induced escalation within the LLM pipeline. Employing a mechanism-centered taxonomy spanning 41 risk patterns, the work disentangles inference-time failure modes using black-box benchmarks across commercial stacks, exposing capability-safety scaling mismatches and motivating zero-trust system design.

Mechanism-Centered Risk Taxonomy and Attack Graph

A key contribution is the formal taxonomy of semantic, structural, and state-based attack vectors, grouped by seven principal mechanism classes: (1) Obfuscation-based, (2) Modality bridging, (3) Interpretive and structural manipulation, (4) State and memory effects, (5) Architectural/ecosystem interactions, (6) Social and reflective steering, and (7) Agentic system risks. Subclasses formalize practical techniques such as base64/leet encoding, visual channel instruction via OCR, morphological/steganographic triggers, non-executable context cues, conditional block seeding, and session-scoped rule injection.

Relationships among these classes, their escalation pathways, and potential for cross-component amplification are represented in the Attack Graph, which systematically captures composition and intersection of mechanisms in deployed pipelines.

Figure 1: Attack Graph illustrating mechanism and escalation relationships across risk pattern classes in LLM-based architectures.

The empirical mapping links each risk pattern to quantitative benchmarks and evidence of cross-model variance, allowing reproducible evaluation regardless of specific provider alignment strategies.

Empirical Findings: Failure Modes and Capability–Safety Scaling

Using standardized, text-only black-box benchmarks under provider-default settings, the paper measures each pattern’s incidence according to metrics such as Decode Success (DS), Interpretation Escalation Output (IEO), Policy Override Behavior (POB), Policy Deviation/Inconsistency (PDI), and Refusal/Redirect (RR). Notable findings include:

• Interpretation-driven Escalation: Models often infer actionable semantics from obfuscated or structurally encoded content, emitting implementation-shaped output (e.g., code scaffolds, stepwise procedures) even without explicit imperatives. For instance, in the “Form-Induced Safety Deviation” pattern, 100% IEO was observed for structurally disguised destructive operations across all models.
• Modality Bridging and Provenance Loss: Visual (OCR) and unimodal outputs are routinely ingested as trusted context, enabling attack vectors where image-embedded instructions, after OCR, bypass input filtering and migrate through the pipeline as system-level directives.

  Figure 2: Example of textual instruction embedded in an image, decoded by OCR into the LLM pipeline.

• Stateful and Delayed Escalation: Context poisoning, session-scoped rule injection, and delayed trigger patterns demonstrate how latent, contextually seeded directives can persist and later escalate on benign prompts, bypassing conventional input-based filters.
• Trust Inheritance and Cascade Effects: Downstream components accept inferred or decoded content as privileged without provenance or revalidation, enabling cascaded privilege escalation and tool invocation if not intercepted by architectural controls.

Architectural Failure Syndromes and Cross-Model Variance

A cross-sectional analysis reveals that architectural vulnerabilities are not a function solely of specific attack strings but of processing mechanisms and context governance. While certain prompt classes universally bypassed naive filters, empirical IEO/POB rates exhibited significant inter-model variance even under identical conditions—implying provider-side alignment and guardrails can moderate but not eliminate root architectural risk.

Capability–Safety Scaling Mismatch: Systems exhibiting higher semantic reconstruction abilities (decode, interpretation) often see correspondingly higher rates of unsafe escalation outputs unless explicit gating is applied after interpretation but before action, underscoring a persistent gap between representational capacity and alignment policy.

Defense Architecture and Zero-Trust Principles

To counter these recurrent architectural failure modes, the paper synthesizes a set of architectural defense principles:

• Provenance Enforcement and Context Sealing: Every context segment is tagged with immutable provenance and privilege metadata, with untrusted inputs routed through decode-only frames prior to validation and any elevation.
• Mandatory Revalidation at Trust Boundaries: Any content entering through decoding, modality bridging, or upstream components re-enters the policy pipeline and cannot directly yield implementation-shaped actions.
• Plan and Tool Verification Gates: Agent plans, tool invocations, and memory writes must cross explicit verification gates checking for both provenance and policy eligibility.
• Semantic Zoning of State and Memory: Context windows, caches, and persistent memory are partitioned into privilege zones with explicit, auditable promotion rules and limited cross-zone inheritance.

  Figure 3: Defense architecture schematic depicting multimodal sandboxes, provenance-tagged context routing, and introspective monitors at the LLM core and agent boundaries.

These principles collectively constrain unvalidated trust propagation, curb interpretation-driven escalation, and harden stateful surfaces against delayed activation. The companion “Countermind” architecture [Schwarz2025Countermind] is posited as an instantiation of these patterns.

Implications and Prospects

The demonstrated architectural failures and systemic trust inheritance patterns necessitate a shift in LLM security from surface-string filtering to mechanism-aware, stateful, and provenance-governed design. Practical implications include:

• Evaluation and Benchmarking: Security benchmarking must move to mechanism-composition coverage, including stateful, cross-stage attacks and contrived modality-bridging probes. Reporting should emphasize confidence intervals, inter-model variance, and rubrics that separate interpretive from execution escalation.
• Ecosystem and Agent Security: Tool- and agent-enabled systems must enforce plan provenance and verification not only for external user input but also for contextually inferred, decoded, or inherited directives, especially when agent policy reprogramming or physical-world actuation is in scope.
• Alignment and Training: Training RLHF or preference optimization pipelines must be inoculated against false-flag feedback and internal reward for inferred but unsafe policy escalation; feedback auditing and consensus/fact separation are mandatory [wang2023rlhfpoison].
• Future Development: As model capacity scales, architectural controls must trigger at, or ahead of, inference milestones where new semantic material is internalized, not merely at input boundaries or output emission.

Conclusion

The paper provides substantial empirical and architectural evidence that LLM-centric AI stacks, as currently deployed, exhibit cross-stage vulnerabilities rooted in systemic trust inheritance, interpretive plan elevation, and privilege escalation through state/memory effects. The demonstrated residual risk—even under diverse provider guardrails and alignment—mandates a paradigm of zero-trust context management, explicit provenance enforcement, mandatory plan/tool gating, and ensemble introspective defense (2510.27190). The resulting design patterns should serve as foundational reference for both red-teaming and the engineering of secure LLM-integrated systems, with implications extending to multimodal, tool-enabled, and agentic AI.

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References:

All literature references, method comparisons, and empirical results cited are directly from the primary source (2510.27190) and its extensive reference list.