Pamir AI — agentic threat model
Pamir AI's edge-based, offline architecture significantly reduces remote network attack surfaces, but its deployment in critical sectors like healthcare and manufacturing for real-time decision-making introduces high physical tampering and localized operational risks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.60 |
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 — The specific foundation models used on the edge hardware are not disclosed. Threats include model stealing via physical access to the Distiller hardware, and adversarial manipulation of local sensor inputs.
Not certain from the listing — Details on local vector databases or training data pipelines are omitted. The primary threat is local data poisoning or physical extraction of sensitive training/RAG data stored on the edge device.
Not certain from the listing — The orchestration framework for real-time decision-making is unspecified. Threats involve insecure tool integration with local hardware actuators or sensors, and memory corruption on resource-constrained edge devices.
Pamir AI deploys on proprietary edge hardware (Distiller series) and software, operating offline. The primary threats are physical tampering, side-channel attacks, and the difficulty of deploying security patches to disconnected edge nodes.
Not certain from the listing — While real-time monitoring is a key feature, the observability stack for model drift and security logging in an offline environment is not detailed, creating potential blind spots for security teams.
Not certain from the listing — Although marketed as privacy-preserving, specific compliance certifications (e.g., HIPAA for healthcare, ISO 27001) or local access control mechanisms are not detailed.
Not certain from the listing — There is no mention of multi-agent coordination or marketplace integrations; the system appears to operate as isolated edge nodes.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.