Rate My Professors — agentic threat model
This agent acts as a read-only search and retrieval assistant for professor reviews, presenting minimal agentic risk due to its lack of write access, lack of execution tools, and low autonomy.
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 | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| 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 — likely relies on a standard commercial or open-source LLM for processing user queries and summarizing reviews. Primary threats are prompt injection to bypass search constraints or generate biased summaries.
The agent relies on a structured database of professor reviews and university listings. Key threats include database scraping, data poisoning via fake reviews submitted to the underlying platform, and retrieval inaccuracies.
Not certain from the listing — likely uses a simple retrieval-augmented generation (RAG) framework to fetch and format reviews. Risk of tool misuse is low as tools are restricted to read-only database queries.
Not certain from the listing — standard web hosting and API infrastructure. Primary threats are typical web application vulnerabilities, denial of service, and unauthorized API scraping.
Not certain from the listing — requires monitoring to detect biased summarization, hallucinated professor ratings, or systematic extraction of the underlying database by competitors.
Not certain from the listing — compliance risks are low as the agent processes public directory data, though it must comply with standard data privacy laws regarding student-generated content.
The agent operates as a standalone vertical search tool with no multi-agent coordination or ecosystem integration described, resulting in negligible ecosystem risk.
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.