OpenAI o1 — agentic threat model
OpenAI o1 is a highly capable foundation model with advanced reasoning and chain-of-thought capabilities, presenting low direct agentic risk due to a lack of autonomous tool execution in its base form, but posing moderate indirect risk if integrated into downstream agentic workflows without external guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.70 |
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.
As a closed-source foundation model, o1 is susceptible to advanced adversarial jailbreaks, model extraction/stealing, and output misalignment, though the listing highlights 'improved jailbreak resistance' as a key mitigation.
Not certain from the listing — The training data pipeline, fine-tuning datasets, and potential RAG integrations are not detailed, leaving threats like training data poisoning or membership inference unverified but highly plausible.
Not certain from the listing — The listing describes o1 as a model series rather than an agentic framework; threats involving tool misuse, memory poisoning, or insecure orchestration depend entirely on the external framework hosting the model.
Not certain from the listing — No details are provided regarding hosting infrastructure, API sandboxing, or network security controls, though API key exposure and cloud infrastructure compromise remain standard threats.
The model incorporates 'Self-fact-checking' and 'RLHF' as internal alignment and observability mechanisms. Threats include evaluation gaming, bypass of internal guardrails, and the opacity of the hidden chain-of-thought reasoning process.
Not certain from the listing — No compliance standards (e.g., SOC2, ISO, EU AI Act alignment) or enterprise identity/access management policies are specified in the directory listing.
Not certain from the listing — The listing does not mention multi-agent coordination, marketplace integrations, or ecosystem-level interactions, meaning cascading agent-to-agent trust failures are dependent on downstream implementations.
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.