mycrab.space — agentic threat model
mycrab.space is an infrastructure utility rather than an active AI agent, posing high deployment and network security risks by exposing local development environments (localhost) to the public internet without apparent built-in security controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.00 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.00 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.00 | |
| Opacity & Reflexivity | 0.00 |
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 — mycrab.space is a deployment/tunneling tool and does not bundle or specify foundation models.
Not certain from the listing — The service acts as a network tunnel/hosting provider and does not manage training data, vector stores, or RAG pipelines directly.
Not certain from the listing — It claims 'no framework, build, or configuration required' to expose agents, meaning it is framework-agnostic and does not provide orchestration itself.
Exposing localhost to a live public address without explicit configuration or sandboxing can lead to severe infrastructure compromise, local network exposure, and unauthorized access to local developer environments.
Not certain from the listing — There is no mention of built-in logging, monitoring, guardrails, or evaluation metrics for the traffic passing through the tunnel.
The listing emphasizes zero configuration and quick setup, suggesting a lack of built-in authentication, access control, or compliance guardrails for the exposed endpoints.
By providing stable public web addresses for local agents, it facilitates multi-agent and external API integrations, but also increases the attack surface for rogue agents or external entities to interact with the local agent.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).