FinRobot — agentic threat model
FinRobot is an open-source financial agent framework with high planning and multi-agent capabilities, presenting significant risks of financial data poisoning, tool misuse, and cascading failures in multi-agent workflows if deployed without strict sandboxing and input validation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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.
Integrates multi-source LLMs, exposing the platform to model-specific vulnerabilities such as prompt injection, adversarial examples, and misaligned outputs across different foundation models.
Processes real-time financial data, making it highly susceptible to data poisoning, API manipulation, and downstream decision corruption if ingestion sources are compromised.
Uses Financial Chain-of-Thought (CoT) and customizable agents, which introduces risks of tool misuse, insecure tool integration, and logic flaws in multi-step financial reasoning.
Not certain from the listing — deployment details, hosting environments, secrets management, and sandboxing mechanisms are not specified in the public directory listing.
Not certain from the listing — there is no explicit mention of evaluation frameworks, real-time monitoring, guardrails, or drift detection for the financial models.
Not certain from the listing — compliance with financial regulations, identity management, and access control policies are not detailed in the open-source description.
Supports customizable, specialized AI agents in a multi-layered architecture, creating risks of cascading failures, trust abuse between agents, and rogue agent behavior within the ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).