In plain English
This page shows what kind of support exists for each claim: real systems, experiments, early evidence, architectural reasoning, open questions, or speculative scenarios.
- 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.
Source Report — Threat Assessment of Modular AI Systems Capable of Autonomous Generation, Deprecation, and Algorithmic Reproduction
This evidence page summarizes an uploaded report that informed the v1.8.0 content expansion. It is a source dossier, not an independently verified consensus source.
Claim
Report mapping algorithmic mitosis, meiosis, autonomous deprecation, endogenous yardstick driftAssurance decay caused when the system or adjacent automation changes the measurements, thresholds, tests, or evaluator assumptions used to judge success. Open glossary definition, and execution-time controls.
Source and preservation
| Field | Value |
|---|---|
| Source type | Uploaded Markdown source report |
| Raw preservation path | docs/source-reports/raw-markdown/ai-self-replication-threat-assessment.md |
| Public-safe summary path | docs/source-report-summaries/ai-self-replication-threat-assessment.md |
| Date last reviewed in UTC | 2026-06-27T00:35:00Z |
| Evidence category | Architectural inferenceA conclusion or output produced from data. Open glossary definition |
What the evidence does show
The report provides a structured conceptual treatment that can be used to expand Cognivirus.com terminology, risk maps, and control guidance.
What the evidence does not show
The report alone does not prove that every named scenario is current, universal, or independently replicated. Claims about specific incidents, attack success rates, or future timelines require primary-source review before being presented as demonstrated fact.
Site interpretation
This report contributes to the v1.8.0 expansion through public-safe concepts: transition graphs, adapter reproduction boundaries, persistence reservoirs, evaluatorA system that judges whether an AI output or candidate is acceptable. Open glossary definition drift, execution-time control, and human-incentive safety boundaries.