Parasitic AI Field Guide
Host. Vector. Payload. Propagation. Containment.
ParasiticAI.com studies AI-mediated systems that appear to gain persistence, spread, or optimization advantage by extracting from hosts: attention, trust, corpora, ranking signals, training data, infrastructure, and defenses.
The archive is intentionally educational and defensive. It explains mechanisms at the concept level without publishing exploit code, medical advice, legal conclusions, or claims of certification.
How to use this guide
Start with the host-extraction tests, then choose the pathway that matches the system under review. The goal is practical analysis, not sensational labeling.
- Name the host and the resource being extracted.
- Name the feedback loop that lets the system persist or spread.
- Name the measurable degradation or uncertainty.
- Choose containment moves that preserve autonomy, source diversity, provenance, and least privilege.
Source map
Read every source at the right strength.
Parasitic AI and persona-parasitology essays
Useful for naming patterns, seed prompts, and anecdotal case clusters. Treat as hypothesis-generating.
Sycophancy, sensitive-conversation, and model-release notes
Useful for deployment failures, mitigation direction, and provider-side safety vocabulary.
Model collapse, retrieval collapse, prompt injection, long-context safety, and mental-health chatbot studies
Useful for mechanisms, benchmarks, and reproducible evaluation design.
NIST AI RMF profile, EU AI Act transparency rules, copyright reports and litigation
Useful for governance posture, source transparency, and market-structure questions.
AI misuse, agentic security, RAG poisoning, and non-human identity reports
Useful for defensive indicators, but often telemetry-dependent and incomplete.
Measurement table