Llama 3.3 — agentic threat model
Llama 3.3 is a highly capable foundation model with built-in safety guardrails (Llama Guard) but low native autonomy, making its security posture highly dependent on the external application framework and deployment environment.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.60 | |
| 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.
As a 70B parameter foundation model, Llama 3.3 is susceptible to adversarial prompt injection, jailbreaking, and model utility exploitation. However, it benefits from RLHF and the integrated Llama Guard safety framework to mitigate misaligned outputs.
Not certain from the listing — The listing mentions a 128k context window and synthetic data generation, but does not specify the RAG architecture, vector database, or data ingestion pipelines used in deployment.
Not certain from the listing — Llama 3.3 is an instruction-tuned model rather than an active agent framework; orchestration, tool execution, and state management depend entirely on external frameworks.
Not certain from the listing — No details are provided regarding hosting infrastructure, API gateway security, containerization, or sandboxing environments for model execution.
The model natively supports the Llama Guard safety framework for input/output content moderation, though runtime logging, drift detection, and evaluation monitoring must be configured externally.
Not certain from the listing — Compliance certifications (e.g., SOC2, ISO 27001) and enterprise identity/access management controls are not specified in the model's public directory listing.
Not certain from the listing — The listing describes a standalone model and does not detail multi-agent coordination protocols, marketplace trust boundaries, or agent-to-agent authentication.
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.