Ungate AI — agentic threat model
Ungate AI presents a high-risk profile due to its focus on decentralized, multi-agent coordination ('Internet of Agents') built on blockchain infrastructure (EigenLayer). The primary risks stem from cascading multi-agent failures, trust abuse in agent-to-agent communication, and the financial/operational impact of smart contract or protocol-level compromises.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.90 | |
| Multi-Agent Interactions | 1.00 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.80 |
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 — No specific foundation models are mentioned. General risk: Underlying LLMs used by coordinating agents could be vulnerable to adversarial prompt injection or model reprogramming, potentially disrupting the coordination protocol.
Not certain from the listing — The listing does not detail data storage, RAG, or vector databases. General risk: Poisoning of discovery registries or metadata could lead to routing traffic to malicious agents.
The agent framework is a composable network built on EigenLayer. Threats include insecure tool integration, protocol-level vulnerabilities in the 'Internet of Agents' protocol, and malicious agent registration.
Built on EigenLayer (blockchain infrastructure). Threats include smart contract vulnerabilities, consensus manipulation, validator compromise, and infrastructure-level exploits of the decentralized network.
Not certain from the listing — No monitoring, logging, or guardrail systems are described. General risk: Lack of centralized observability in a decentralized multi-agent network makes detecting rogue agent behavior or cascading failures extremely difficult.
Security relies on EigenLayer's restaking/slashing mechanisms for economic security. However, traditional identity, authorization, and regulatory compliance (e.g., EU AI Act, NIST) are not detailed.
This is the core of Ungate AI ('Internet of Agents'). Extreme threats of multi-agent trust abuse, rogue/compromised agents coordinating malicious activities, cascading failures across the composable network, and sybil attacks.
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.