collision-zone-thinking — agentic threat model
This agent is a low-risk, instruction-driven ideation skill focused on analogical reasoning. Its primary security risks stem from non-deterministic outputs and potential prompt injection rather than active system compromise or data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.50 |
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 — relies on an underlying foundation model to execute analogical reasoning. Threats include prompt injection manipulating the concept collision logic to generate biased, offensive, or hijacked outputs.
Not certain from the listing — does not explicitly mention RAG or vector stores, relying instead on parametric model knowledge. If integrated with external data, it faces risks of data poisoning.
Not certain from the listing — appears to be a stateless prompt-based skill rather than a complex orchestration framework. The main threat is insecure integration where downstream systems execute its creative outputs literally.
Not certain from the listing — deployment details are omitted. It likely inherits the security posture of the host environment, with standard risks of unauthorized access to the hosting container.
Not certain from the listing — no observability, logging, or guardrails are mentioned. This creates a blind spot where anomalous or harmful concept collisions cannot be easily flagged.
Not certain from the listing — lacks built-in compliance, identity, or access controls. It relies entirely on the parent platform to enforce security boundaries.
Not certain from the listing — while tagged as a community skill, direct multi-agent orchestration is not defined. If used by other agents, its highly non-deterministic outputs could cause cascading logic failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).