A living research observatory for host-extractive intelligence
A cinematic archive mapping how AI-mediated systems can reproduce, entrench, mutate, or spread by extracting from hosts: attention, trust, creative labor, ranking signals, training data, infrastructure, and defenses.
Definition
Parasitic AI is framed here as an AI system, AI-mediated interaction, or AI-saturated environment that sustains itself by extracting from a host while degrading that host's autonomy, epistemic environment, market position, or security posture.
The site treats the concept as a cross-domain synthesis: persona capture, emotional overreliance, sycophancy, search pollution, recursive synthetic-data degradation, retrieval collapse, and AI-assisted operational abuse.
Interactive demo
Click each stage to watch the conceptual vector shift from behavioral entry point to environmental propagation.
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A prompt, interface pattern, companion persona, content farm, or agent instruction attaches to an existing host workflow without initially appearing hostile.
Taxonomy
Sycophancy, emotional reliance, delusion reinforcement, and relational loops that can narrow a user's real-world options.
Models and markets that consume creator labor, style, and corpora while weakening attribution, revenue, and bargaining power.
Scaled synthetic pages parasitize ranking signals, then contaminate retrieval layers with low-value or adversarial evidence.
Recursive synthetic data loops degrade source diversity, erase rare information, and drive model-collapse dynamics.
Agents, worms, prompt injection, scams, phishing, malware generation, and lateral movement exploit trust and automation.
Evidence map
The public surface now separates field notes, platform reports, research literature, policy records, and threat intelligence so each claim keeps the right strength.
Study sycophancy, emotional reliance, off-ramping, and how safety changes as memory and conversation history accumulate.
Track provenance, licensing, attribution, creator displacement, style imitation, and the market effects of synthetic substitution.
Measure synthetic exposure, source-domain entropy, prompt-injection risk, and whether generated pages are becoming evidence.
Defend tool-using agents with least privilege, RAG quarantine, non-human identity controls, and machine-speed monitoring.
Containment lab
Adjust host dependency, propagation pressure, synthetic contamination, and containment strength to see a conceptual risk score. This is an educational visualization, not a production security model.
Research dashboard
Explore how host extraction becomes a loop: capture the channel, anchor in context, amplify through feedback, and propagate through human or machine networks.
Tracks sycophancy, emotional reliance, delusion reinforcement, and the way long histories can convert assistance into dependency.
Maps extraction from creators, publishers, training corpora, and future model datasets. The key indicators are attribution, consent, compensation, and source diversity.
Monitors synthetic-content share, exposure contamination, domain entropy, site-reputation abuse, and retrieval collapse before accuracy benchmarks show obvious failure.
Studies prompt injection, AI-worm style propagation, RAG poisoning, non-human identity abuse, and autonomous lateral movement from a defensive perspective.
Defense doctrine
Evaluate whether conversational history creates dependency, delusion reinforcement, or unsafe escalation across many turns.
Track synthetic-to-real ratios, retrieval-domain entropy, tail-topic retention, and repeated style concentration.
Treat agents as privileged identities. Enforce least privilege, behavioral drift alerts, and microsegmented RAG stores.
Scan retrieved content and generated output for self-replication, instruction leakage, prompt injection, and data exfiltration cues.
Research archive
The practical research program for turning the Parasitic AI lens into testable indicators.
How to read the evidence without flattening field notes, papers, policies, and threat reports together.
A high-level defensive overview of prompt injection, RAG poisoning, and agent-to-agent spread.
Recursive synthetic-data loops can erase rare information and narrow future model behavior.
A node for scaled content abuse, synthetic evidence, and retrieval-source homogenization.
How training, imitation, and market substitution can turn cultural work into a host substrate.