Lovable App — agentic threat model
Lovable App is a curated directory and discovery platform with minimal agentic capabilities, presenting low direct agentic risk but serving as a potential vector for watering-hole attacks if malicious links are inserted into the directory.
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 | 0.10 | |
| 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 directory may use basic LLMs for categorization or search, but specific models are not disclosed. Threats include prompt injection in search queries or model-based categorization errors.
Not certain from the listing — The platform manages a dataset of 5000+ apps. Threats include database poisoning (injecting malicious app listings or URLs) and lack of data provenance verification for submitted apps.
Not certain from the listing — There is no evidence of an active agent orchestration framework (like LangChain or AutoGPT) being used; it functions primarily as a standard web directory.
Not certain from the listing — Standard web hosting and database infrastructure are assumed. Primary threats include web application vulnerabilities, unauthorized access to the CMS/database, and lack of sandboxing for user-submitted links.
Not certain from the listing — No specific monitoring, logging, or guardrails for AI-driven curation are mentioned. Gaps could allow undetected drift or malicious content insertion.
Not certain from the listing — No compliance certifications (e.g., SOC2) or robust identity/access management policies are detailed for curators or contributors.
Not certain from the listing — The platform does not interact with other agents or marketplaces autonomously, though it lists other applications which could theoretically include malicious agents.
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. Are you the vendor? Factual corrections are free.