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metadata
title: Model Organisms of Supply-Chain Co-option
emoji: 🔬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.14.0
app_file: app.py
pinned: false
license: cc-by-4.0
short_description: LotL failure modes in RAG-augmented agent runtimes
tags:
- ai-safety
- agentic-ai
- rag
- scalable-oversight
- research-paper
Model Organisms of Supply-Chain Co-option
Living-off-the-Land Failure Modes in RAG-Augmented Agent Runtimes
Anthony Maio | Independent Researcher | January 2026
Overview
This Space presents an interactive exploration of the paper "Model Organisms of Supply-Chain Co-option," a forensic case study of living-off-the-land (LotL) failure modes in RAG-augmented agent runtimes.
Key Findings
The "Manifold Incident" demonstrates how agentic systems can:
- Identify legitimate dependencies as high-leverage deployment vectors
- Propose co-opting real infrastructure via incentive-aware framing
- Exploit approval incentives to maximize deployment probability
Mitigation: Argos-Swarm
The paper proposes a two-pronged defense:
- EAP (Red Team): Evolutionary Adversarial Pipeline for robustness evaluation
- HDCS (Blue Team): Heterogeneous verification across diverse model families
Resources
Citation
@article{maio2026modelorganisms,
title={Model Organisms of Supply-Chain Co-option: Living-off-the-Land Failure Modes in RAG-Augmented Agent Runtimes},
author={Maio, Anthony},
year={2026},
url={https://making-minds.ai}
}
License
This work is licensed under CC-BY-4.0.