Naut — agentic threat model
Naut is an early-stage personal assistant with extremely limited public details, presenting a highly uncertain risk profile that likely involves standard personal data exposure and tool integration risks typical of LLM-based assistants.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
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 — likely relies on third-party commercial LLMs (e.g., OpenAI, Anthropic) which are vulnerable to prompt injection, adversarial examples, and data leakage if not properly sandboxed.
Not certain from the listing — as a personal assistant, it will likely ingest user personal data, raising risks of data poisoning, unauthorized access, and lack of data lineage controls.
Not certain from the listing — orchestration details are unknown, but standard personal assistant frameworks risk tool misuse (e.g., executing unintended actions via email/calendar APIs) and memory poisoning.
Not certain from the listing — hosting and sandboxing mechanisms are unspecified, presenting potential risks of container compromise or insecure secrets management for user integrations.
Not certain from the listing — there is no mention of guardrails, real-time monitoring, or evaluation frameworks to detect drift, anomalies, or malicious inputs.
Not certain from the listing — compliance posture (e.g., GDPR, SOC2) is completely unstated, and identity/authorization controls for accessing user accounts are undefined.
Not certain from the listing — it is unclear if Naut interacts with other agents or marketplaces, which would introduce risks of cascading failures and agent-to-agent trust abuse.
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.