TickersFeed — agentic threat model
TickersFeed acts as a stateless financial data provider for AI agents using x402 micropayments. While its internal agentic capabilities are minimal, its role as a critical data source for autonomous trading bots makes it a high-value target for data poisoning and payment-system exploitation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.10 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.10 | |
| 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 — TickersFeed is a structured data API rather than an LLM or foundation model, though it is designed to feed downstream LLMs.
As a provider of stock, crypto, and DeFi market data, the primary threat is data poisoning or manipulation of the feed, which could lead downstream trading agents to make catastrophic financial decisions.
Not certain from the listing — TickersFeed acts as an external tool rather than hosting an agent framework itself, but insecure integration by client agents could lead to tool misuse or unexpected execution costs.
The infrastructure relies on the x402 micropayment protocol for pay-per-call access. Threats include API denial-of-service, payment gateway exploitation, or host compromise affecting data delivery.
Not certain from the listing — There is no mention of data validation guardrails, drift detection, or observability logging to ensure the integrity of the financial data being served.
By eliminating traditional API keys and user accounts in favor of anonymous x402 micropayments, the service lacks traditional identity and access management (IAM), making auditability and abuse prevention challenging.
Designed specifically for autonomous AI agents and automated systems. A compromise or manipulation of this feed represents a systemic risk that can cause cascading financial failures across the connected agent 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.