Space Bunny — agentic threat model
Space Bunny is a reasoning-focused developer tool and API with low autonomy due to explicit user-controlled tool execution, but it presents moderate risk via its large context window, multimodal inputs, and tool-calling capabilities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| 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.
Utilizes Space Bunny Alpha, an anonymous preview reasoning model supporting text, image, and video inputs. Primary threats include multimodal prompt injection, adversarial inputs designed to bypass safety filters, and model reprogramming via complex reasoning prompts.
Not certain from the listing — The model supports a 1-million-token context window and long-document analysis, which introduces risks of data exfiltration, sensitive data exposure within the context, and potential data leakage if session inputs are logged or used for training.
Supports tool calling and structured JSON outputs. While the risk of unauthorized tool execution is mitigated by user-controlled execution, threats still exist around the generation of malicious tool payloads or indirect prompt injection manipulating the tool-calling logic.
Not certain from the listing — Hosted as an online playground and OpenAI-compatible API. Standard infrastructure threats apply, including API key exposure, lack of rate limiting, and potential sandbox escape if the playground executes user code server-side.
Not certain from the listing — No built-in guardrails, logging, or evaluation frameworks are detailed, though the model allows adjusting reasoning effort which may impact predictability and output drift.
Not certain from the listing — There is no mention of compliance certifications, enterprise access controls, or data privacy guarantees for the anonymous preview model.
Not certain from the listing — The agent operates as a standalone API and playground with no described multi-agent orchestration or ecosystem integration.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.