In plain English
This page is part of the technical reference. It keeps the expert detail but starts with a plain-language summary for first-time readers.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Most Likely Threat Expansion Record
Version 1.14.0 adds a dedicated threat-model section explaining the most likely CognivirusA behavior pattern that can survive, move, or reappear across a changing AI system. Open glossary definition threat: distributed behavioral persistence through modular AI systems.
What changed
- Added top-level Most Likely Threat Model route.
- Added detailed pages for the exact threat, mechanism, likelihood, entry points, early warnings, defensive review, and uncertainty.
- Added Most Likely Threat Map.
- Added Most Likely Threat Review Worksheet.
- Added new interactive flowchart and schematic components.
- Updated header, footer, rail links, sitemap, search index, AI manifests, and
.uaimemory.
Content boundary
The expansion is defensive and non-operational. It explains how a behavior can persist across carriers without providing exploit steps, payloads, evasion procedures, replication instructions, credential theft instructions, or backdoor construction guidance.
Source-report basis
The expansion synthesizes the preserved report corpus on self-replicating multi-LoRA ecosystems, autonomous self-replication threat assessments, teleodynamic Feed/Fork/Fight/FleeA simple loop for adaptive system change. Open glossary definition loops, adapter composition, evaluator drift, and aggressive mutualism boundaries.