Assisterr AI — agentic threat model
Assisterr AI presents a unique risk profile centered on its decentralized SLM marketplace and data exchange, where supply chain risks like model poisoning and IP theft are prominent. While blockchain offers governance transparency, the closed-source nature of the platform and lack of detailed runtime sandboxing information elevate its overall security exposure.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.60 | |
| Non-Determinism | 0.60 | |
| 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.
Focuses on Small Language Models (SLMs). Key threats include model stealing or unauthorized replication of proprietary SLMs within the marketplace, as well as adversarial attacks or backdoors embedded in user-submitted models.
Features a dedicated data marketplace for accessing datasets. This introduces significant risks of data poisoning, provenance/lineage gaps, and the potential ingestion of intellectual property or personally identifiable information (PII) without consent.
Not certain from the listing — The specific orchestration framework, memory mechanisms, and tool-calling capabilities of the hosted SLMs are not detailed, leaving potential gaps regarding insecure tool integration or memory poisoning.
Not certain from the listing — While blockchain is used for decentralized governance, the underlying hosting infrastructure, execution sandboxing for SLMs, and secrets management protocols are not specified.
Not certain from the listing — Mentions 'real-time optimization' but does not detail the specific evaluation frameworks, guardrails, or anomaly detection systems used to monitor SLM outputs and detect drift.
Not certain from the listing — Blockchain technology is leveraged for decentralized model governance and transparency, but traditional enterprise security controls, compliance certifications (e.g., SOC2), and granular access policies are not explicitly defined.
The platform operates as a decentralized AI ecosystem with an integrated marketplace. This creates a high exposure to ecosystem-level threats, such as rogue or compromised agents interacting with other models, monetization fraud, and cascading failures across interconnected SLMs.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).