List Bulb — agentic threat model
List Bulb is a static directory and discovery platform with minimal agentic capabilities, presenting a very low risk profile focused primarily on web-based submission handling rather than autonomous execution.
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 | 0.00 | |
| Non-Determinism | 0.20 | |
| Opacity & Reflexivity | 0.10 |
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 — The specific foundation models used to categorize or curate submissions are not disclosed. Standard risks like prompt injection during submission processing could theoretically lead to misaligned categorization.
Not certain from the listing — The directory relies on a database of submitted AI tools and links. The primary threat is data poisoning via malicious submissions designed to inject spam or SEO-manipulating backlinks.
Not certain from the listing — There is no evidence of an active agentic framework or complex orchestration. Risks are limited to basic input validation of the tool submission form.
Not certain from the listing — Standard web hosting infrastructure is assumed. Threats include typical web application vulnerabilities, server misconfigurations, and lack of isolation for submission processing.
Not certain from the listing — It is unclear if automated content moderation or evaluation guardrails are in place to filter out malicious URLs or spam submissions before they are published.
Not certain from the listing — Access controls and submission verification mechanisms are not detailed. The platform requires robust moderation workflows to prevent SEO abuse and malicious link injection.
Not certain from the listing — The platform operates as a static directory and does not participate in a multi-agent ecosystem or execute autonomous agent-to-agent interactions.
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.