execution_boundary_lab

Purpose

This repository demonstrates a structural phenomenon:

Information pre-positioning causes cascading execution failures in naive systems.

When structured action bundles contain implicit authority, hidden defaults, ambiguous targets, or overloaded metadata, downstream execution engines can produce deterministic but unintended side effects.

This lab shows:

1. How syntactically valid inputs can corrupt state under naive execution.
2. Why runtime mitigation is insufficient.
3. Why a pre-execution admissibility gate is structurally necessary.

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Why This Matters

Modern AI-integrated systems increasingly execute structured actions proposed by probabilistic models.

Most governance efforts focus on runtime mitigation — detecting and correcting undesirable states after partial execution.

This lab demonstrates a different architectural claim:

If a system permits certain categories of state to form, mitigation becomes an ongoing operational burden.

Reducing admissibility upstream reduces mitigation workload downstream.

This is not an alignment argument. It is a systems design argument.

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What This Repository Is

• A deterministic simulation environment.
• A failure harness.
• A conformance test surface.
• A behavioural contract for admissibility gating.

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What This Repository Is Not

• It does NOT include the Trinity gate implementation.
• It does NOT expose gating logic.
• It does NOT implement scoring heuristics.
• It does NOT provide production security tooling.

This is a boundary demonstration only.

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Core Concept

Information Pre-Positioning

Information pre-positioning occurs when structured data embeds executable consequences that are not explicit at the point of execution.

Examples include:

• Implicit privilege escalation fields.
• Hidden default behaviours.
• Ambiguous resource targets.
• Metadata that alters execution ordering.
• Conflation of descriptive and authoritative fields.

A naive executor processes these structures deterministically, yet produces state corruption.

The failure is not randomness. The failure is admissibility.

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Architectural Distinction

Runtime mitigation:

• Detects and handles problematic states after they form.

Pre-execution admissibility gating:

• Prevents problematic state categories from entering the system.

This repository illustrates why the latter reduces systemic workload and failure surface.

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Repository Structure

/execution_boundary_lab
  README.md
  SPEC.md
  /sim
      __init__.py
      executor.py
      resources.py
      logger.py
  /cases
      contaminated_case_1.json
      contaminated_case_2.json
      contaminated_case_3.json
      contaminated_case_4.json
      contaminated_case_5.json
      contaminated_case_6.json
      clean_case_1.json
  /gate_api
      __init__.py
      interface.py
  /baseline
      __init__.py
      naive_executor.py
  /tests
      __init__.py
      conftest.py
      test_prepositioning_failures.py
      test_gate_contract.py
  /reports
      example_trace_without_gate.txt
      example_trace_with_gate.txt

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Running Tests

pip install pytest
python -m pytest

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Extension

To test a real admissibility gate:

1. Implement the Gate interface defined in gate_api/interface.py.
2. Inject it into the executor pipeline.
3. Ensure all conformance tests pass.

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Position

If a system requires mitigation logic at runtime, it has already admitted instability. Admissibility is upstream of enforcement.

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Licence

Apache 2.0. See LICENSE.