Aurory AI — agentic threat model
Aurory AI presents a high-risk profile due to its integration of autonomous LLM agents with Web3 and on-chain execution capabilities, where vulnerabilities can lead to direct, irreversible financial losses.
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 | 0.80 | |
| Multi-Agent Interactions | 0.70 | |
| Non-Determinism | 0.80 | |
| 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 — The specific LLMs and ML models used are not disclosed. Standard foundation model risks like prompt injection and adversarial manipulation could lead to unintended on-chain transactions.
Not certain from the listing — No details are provided regarding data pipelines, vector databases, or RAG mechanisms. Data poisoning or manipulation of market feeds could severely impact trading analytics.
Not certain from the listing — The orchestration framework for executing tasks like coding and trading is unspecified. Insecure tool integration represents a critical threat if agents can execute arbitrary smart contract calls.
Not certain from the listing — While described as a decentralized platform, the hosting, sandboxing, and key management infrastructure for these on-chain agents are not detailed.
Not certain from the listing — There is no mention of real-time monitoring, transaction guardrails, or anomaly detection to prevent rogue agent behavior on-chain.
Not certain from the listing — The platform is closed-source with no mentioned compliance certifications (e.g., SOC2) or smart contract audits, raising trust and verification concerns.
Not certain from the listing — Although it offers a suite of agents, the mechanisms for agent-to-agent trust, delegation, and preventing cascading failures in a decentralized ecosystem are not described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).