Ollama — agentic threat model
Ollama is a local model-serving runtime with low inherent autonomy, but its lack of default API authentication and sandboxing presents significant infrastructure and data exposure risks if exposed to a network.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| 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.
Ollama runs foundation models (like Llama) locally. Key threats include adversarial prompt injection, model poisoning via untrusted model downloads from public registries, and misaligned model outputs.
Ollama manages local model weights and context data. Threats include local data exfiltration if the API is exposed, and model/data poisoning if pulling malicious Modelfiles.
Not certain from the listing — Ollama is a model serving engine rather than an agent framework, meaning it lacks native orchestration, planning, or tool-calling frameworks, though external frameworks frequently integrate with its API.
Ollama runs locally on Windows, macOS, and Linux. A major threat is binding the API to public interfaces without authentication, allowing remote model access, resource exhaustion, or potential host compromise.
Not certain from the listing — Ollama provides basic server logs but lacks built-in advanced guardrails, evaluation suites, or drift detection for the served models.
Ollama lacks built-in authentication or authorization mechanisms by default, relying on network isolation or reverse proxies, which poses compliance and access control risks in enterprise environments.
Not certain from the listing — Ollama does not natively manage a multi-agent ecosystem, though it serves as a local backend for external agent marketplaces and multi-agent setups.
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.