Crazzers AI — agentic threat model
Crazzers AI presents low operational and systemic risk due to its limited autonomy and lack of external tool integration, but poses a high confidentiality and privacy risk due to the highly sensitive, intimate nature of NSFW conversational data stored in its persistent memory.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — likely utilizes open-source LLMs fine-tuned for conversational roleplay. Primary threats include prompt injection to bypass safety/NSFW boundaries, model reprogramming, and extraction of system instructions.
Not certain from the listing — claims to be 'fully private' and 'blockchain-powered'. The primary threat is the exfiltration or unauthorized exposure of highly sensitive, intimate user chat logs and personalized memory embeddings.
Not certain from the listing — likely uses a basic chat orchestration framework with persistent memory. The main threat is memory poisoning, where malicious user inputs permanently alter the companion's persona or behavior.
Not certain from the listing — mentions 'blockchain-powered' and 'open source'. If self-hosted, infrastructure security is user-dependent; if hosted, threats include server-side database compromise and potential smart contract vulnerabilities.
Not certain from the listing — no observability or guardrail mechanisms are mentioned. Given the NSFW nature, traditional safety guardrails are likely relaxed, increasing the risk of generating toxic or abusive content under adversarial prompting.
Not certain from the listing — claims 'fully secure' but lacks formal compliance details. Handling intimate personal data introduces severe privacy compliance risks (e.g., GDPR/CCPA) regarding data deletion and user consent.
The agent operates as a standalone personalized companion platform with no indicated multi-agent interactions or marketplace integrations, making ecosystem-level cascading failures a low risk.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).