AI Dirs — agentic threat model
AI Dirs is a low-risk informational directory website with minimal agentic capabilities, primarily presenting risks related to web application security and malicious link submissions rather than autonomous agent failures.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| 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 | 0.20 |
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 — if an LLM is used for semantic search or categorization, it faces minor risks of prompt injection or generating misaligned search summaries.
Not certain from the listing — the primary data risk is directory database poisoning, where malicious actors submit links to malware or phishing sites disguised as legitimate AI tools.
Not certain from the listing — the platform likely functions as a standard web application rather than an active agentic framework, meaning orchestration risks are negligible.
Not certain from the listing — standard web hosting vulnerabilities apply, including potential server misconfigurations, lack of DDoS protection, or insecure API endpoints for tool submissions.
Not certain from the listing — there is no evidence of automated content moderation, input validation, or output guardrails to filter out malicious submissions or search queries.
Not certain from the listing — no security compliance, privacy policies, or access control mechanisms are detailed for the submission and review process.
Not certain from the listing — while it catalogs other AI agents, it does not programmatically interact with them, limiting ecosystem risks to passive referral of users to potentially compromised third-party tools.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).