Theoriq AI — agentic threat model
Theoriq AI presents a high-risk profile due to its autonomous DeFi execution, multi-agent swarm orchestration, and direct integration with blockchain financial infrastructure, where exploit payloads can result in immediate, irreversible financial loss.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| 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 — Theoriq supports the integration of various AI models but does not specify the underlying foundation models. Threats include adversarial prompt injection manipulating financial decisions or model reprogramming.
Not certain from the listing — Mentions integration of data sources for trend analysis, but details on vector databases, RAG, or data lineage are absent. Threats include data/oracle poisoning leading to erroneous financial actions.
As a framework for modular, on-chain agents, the orchestration layer is highly critical. Threats include insecure tool integration with DeFi smart contracts, logic flaws in multi-step financial planning, and malicious tool calling.
Not certain from the listing — Mentions 'computational infrastructure' and 'on-chain agents' but lacks details on hosting, sandboxing, or secrets management. Threats include private key exposure and validator node compromise.
Not certain from the listing — No specific guardrails, evaluation frameworks, or transaction monitoring tools are detailed. Threats include blind spots in transaction validation and drift in trend analysis models.
Not certain from the listing — No compliance certifications (e.g., SOC2, ISO) or specific authorization policies are mentioned. Threats include regulatory non-compliance in automated financial services and smart contract vulnerabilities.
Highly relevant as the platform centers on 'AI agent swarms' and 'collaborative intelligence'. Threats include rogue or compromised agents entering the swarm, agent-to-agent trust abuse, and cascading failures in automated DeFi strategies.
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.