ASI:One — agentic threat model
ASI:One presents a high-risk profile due to its combination of autonomous multi-step planning, persistent memory, and Web3/blockchain integration, which could allow a compromised agent to execute unauthorized financial transactions or exfiltrate sensitive user data.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.60 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.90 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.80 | |
| 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 — The specific foundation LLMs used by ASI:One are not detailed, leaving potential vulnerabilities to model-specific prompt injection, adversarial reprogramming, or alignment bypasses unquantified.
Not certain from the listing — While ASI:One features persistent contextual memory, the underlying storage mechanism (e.g., vector databases, local files) and its protection against memory poisoning or unauthorized exfiltration are unspecified.
ASI:One relies heavily on an agentic framework capable of autonomous multi-step planning, tool execution, and memory retention. This introduces significant risks of insecure tool calling, goal hijacking, and indirect prompt injection through external inputs.
Not certain from the listing — The hosting, sandboxing, and runtime environment for executing tools and managing API integrations are not described, raising concerns about potential container escape or host compromise.
Not certain from the listing — There is no mention of built-in guardrails, real-time monitoring, logging, or evaluation frameworks to detect anomalous agent behavior or drift.
Not certain from the listing — Although the description claims the system stays 'under your control,' there are no explicit details regarding authentication, authorization policies, or compliance standards (e.g., SOC2, GDPR).
ASI:One explicitly supports multi-agent collaboration ('collaborate in groups') and Web3/blockchain interactions. This ecosystem exposure introduces severe risks of cascading agent failures, agent-to-agent trust abuse, and unauthorized smart contract execution.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).