# Threat Assessment of Modular AI Systems Capable of Autonomous Generation, Deprecation, and Algorithmic Reproduction

## Public-safe source report summary

Report mapping algorithmic mitosis, meiosis, autonomous deprecation, endogenous yardstick drift, and execution-time controls.

## Evidence handling

This is an uploaded source report preserved for project continuity. It is treated as a **source dossier**, not as independently verified empirical consensus. Public pages may use its concepts after applying Cognivirus editorial controls: no operational exploit instructions, no claims that speculative incidents are confirmed, and explicit distinction between demonstrated research, emerging evidence, architectural inference, and speculative scenarios.

## Concepts extracted for the site

- Modular AI ecologies should be assessed as transition graphs rather than isolated artifacts.
- Reproduction can mean functional persistence through adapters, descendants, memories, synthetic data, routes, or registries.
- Algorithmic mitosis and meiosis are useful educational metaphors when clearly labeled as metaphors.
- Selection pressure, evaluator drift, and rollback incompleteness are treated as governance hazards.
- Human incentive capture and aggressive mutualism are discussed only as risk models and counterexamples, not as design goals.

## Direct excerpt for reviewer orientation

> # **Threat Assessment of Modular AI Systems Capable of Autonomous Generation, Deprecation, and Algorithmic Reproduction** ## **Introduction to the Paradigm of Autonomous Evolution** The architectural landscape of artificial intelligence is currently undergoing a highly disruptive transition, evolving from static, monolithic structures into highly dynamic, modular systems. This paradigm shift has birthed artificial intelligence architectures capable of autonomous replication, continuous self-improvement, and deep structural self-modification. This foundational transformation introduces a host of unprecedented threat vectors that fundamentally challenge contemporary cybersecurity paradigms, computational resource management protocols, and overarching artificial intelligence safety frameworks.1 At the absolute core of this technological evolution is the emergence of systems possessing Auton

## Site interpretation

The report expands Cognivirus.com by adding more precise taxonomy around adapter reproduction, composition-triggered behavior, persistence reservoirs, execution-time control, and human-incentive boundaries. It does not change the site definition of “cognivirus”: an analytical metaphor for persistent cognitive patterns in adaptive model ecologies.