AutoML-Agent — agentic threat model
AutoML-Agent presents a high-risk profile due to its end-to-end automation capabilities spanning data ingestion to model deployment. The multi-agent architecture introduces cascading trust risks, where a compromise in planning or data handling can directly lead to unauthorized infrastructure deployment.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 1.00 | |
| 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.
Not certain from the listing — Uses LLMs for orchestration and planning, making it vulnerable to prompt injection that could hijack the planning phase or bypass verification stages.
Handles data ingestion and diverse data modalities. Vulnerable to data poisoning during ingestion, which could lead to poisoned ML models or exploit vulnerabilities in data parsing libraries.
Multi-agent framework with specialized agents (prompt, data, model, operation) and retrieval-augmented planning. Vulnerable to planning manipulation, tool misuse (especially by the operation or data agent), and state-desynchronization between agents.
Not certain from the listing — Automates model deployment and operations, which implies access to deployment infrastructure. If unsandboxed, compromised agents could execute arbitrary code on the hosting infrastructure or deploy malicious models.
Features multi-stage verification to ensure deployable solutions. However, if the verification agent itself is bypassed or manipulated, faulty or malicious models could be deployed without detection.
Not certain from the listing — Being open-source, it lacks built-in compliance frameworks, RBAC, or audit logging out of the box, leaving these controls entirely to the deployer.
Uses a multi-agent architecture (prompt, data, model, operation agents). Vulnerable to agent-to-agent trust abuse, where a compromise of one agent (e.g., prompt agent) cascades to compromise the operation or deployment agent.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).