Filtyr AI — agentic threat model
Filtyr AI presents a moderate-to-high agentic risk due to its automated moderation and fraud prevention capabilities, which directly impact marketplace transactions and user data. Its adaptive learning from real-time feedback introduces potential vectors for data/memory poisoning and non-deterministic decision-making.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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 underlying foundation models are unspecified. However, they are highly susceptible to adversarial prompt injection designed to bypass moderation filters or trick the fraud detection system.
Integrates directly with sensitive marketplace data, including user-generated content, listings, and secure messaging. This creates a high-value target for data exfiltration and exposes the system to data poisoning via malicious user submissions.
Orchestrates moderation tasks through agents that adapt via real-time feedback. This feedback loop introduces a risk of memory poisoning, where malicious actors systematically train the agent to accept fraudulent behavior.
Not certain from the listing — hosting, API sandboxing, and network isolation details are not provided. Compromise at this layer could allow attackers to intercept secure marketplace messaging streams.
Not certain from the listing — while it monitors real-time feedback, the presence of robust guardrails, drift detection, or audit logs to catch adversarial manipulation is unconfirmed.
Not certain from the listing — no specific compliance standards (e.g., SOC2, GDPR compliance for automated decision-making) or access control mechanisms are detailed.
Enables the creation of multiple specialized agents (e.g., listing compliance, fraud detection). A compromise in one agent could lead to cascading trust abuse across the marketplace ecosystem.
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.