weever.ai — agentic threat model
weever.ai is a low-risk shopping assistant primarily focused on information retrieval and summarization, with its main security exposures residing in potential data poisoning of ingested reviews and prompt injection leading to affiliate link manipulation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
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 utilizes commercial LLMs for summarization and natural language search. The primary threat is prompt injection via malicious product descriptions or user queries designed to hijack the recommendation engine.
Not certain from the listing — ingests external review data from third-party platforms like Reddit and Amazon. This creates a significant vulnerability to data poisoning, where malicious actors can manipulate reviews to artificially boost or tank product recommendations.
Not certain from the listing — orchestrates search, summarization, and favorite-saving capabilities. Risks include insecure tool integration with retailer APIs and potential memory poisoning within the user's saved favorites profile.
Not certain from the listing — deployed as a closed-source web application. Standard web application security risks apply, including potential unauthorized access to user accounts and saved product lists.
Not certain from the listing — no public details on evaluation guardrails, drift monitoring, or logging mechanisms to detect anomalous recommendation behavior or adversarial inputs.
Not certain from the listing — closed-source, free consumer tool with no explicit compliance certifications (such as SOC2) or robust access control policies detailed in the public directory.
Not certain from the listing — operates primarily as a standalone consumer assistant with retailer integrations, presenting minimal multi-agent ecosystem risks beyond standard API trust boundaries.
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.