Agent Genesis — agentic threat model
Agent Genesis is an open-source, no-code agent framework that lowers the barrier to deploying AI agents with access to diverse data sources and tools, presenting a high risk of insecure default configurations, tool misuse, and data exposure if deployed without rigorous external sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.70 | |
| 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 framework is model-agnostic and does not specify default foundation models, leaving it vulnerable to model-specific risks like prompt injection, adversarial reprogramming, or alignment failures depending on the user's choice.
The framework supports integration with multiple data sources, creating significant risks of data poisoning, unauthorized data access, and lack of lineage tracking if the connected databases or vector stores lack robust access controls.
As an orchestration framework, it is highly susceptible to insecure tool integration, prompt injection leading to unauthorized tool execution, and logic flaws in agent planning or memory management.
Not certain from the listing — While 'easy deployment' is advertised, the listing does not specify if deployed agents run in secure, isolated sandboxes, raising the risk of host compromise or lateral movement if an agent is hijacked.
The listing mentions 'comprehensive management tools' but does not explicitly confirm security-focused observability, guardrails, or anomaly detection to catch malicious agent behavior or drift.
Not certain from the listing — There is no mention of built-in authentication, role-based access control (RBAC), enterprise policy enforcement, or compliance certifications (e.g., SOC2, ISO) for the deployed agents.
The framework allows the creation of multiple customizable agents, which introduces risks of cascading failures, unauthorized agent-to-agent communication, and trust abuse if agents interact without strict boundary controls.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).