What is MAESTRO Threat Modeling?

MAESTRO is a threat-modeling framework purpose-built for “agentic AI” systems—autonomous, tool-using AI agents—so security teams can identify, assess, and mitigate AI-specific risks across the full lifecycle. It stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome.

Why MAESTRO is Essential for Agentic AI Security

• Closes gaps in classic methods. STRIDE/PASTA/LINDDUN/VAST remain useful, but they miss agent-specific issues like adversarial ML, data poisoning, model extraction, agent autonomy/misalignment, and multi-agent interaction risks (collusion, sybil identities). MAESTRO is designed to cover those.
• Built for agent ecosystems. It explicitly models agents interacting with users, tools, other agents, and their environment, where many real-world failures emerge.

How MAESTRO Structures Threat Models

• Seven-Layer Reference Architecture of MAESTRO Threat Modeling. From foundation models and data operations, through agent frameworks, deployment infrastructure, and a vertical security/compliance layer, up to the agent ecosystem and evaluation/observability—so you can reason about threats within and across layers.

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• Layer-Specific & Cross-Layer Threats in MAESTRO. It catalogs threats per layer (e.g., agent impersonation, tool misuse, marketplace manipulation at the ecosystem layer) and cross-layer issues like supply-chain attacks, lateral movement, and goal-misalignment cascades.

Using MAESTRO Threat Modeling in Practice

1. Decompose the system using the seven layers (define agent goals, tools, and interactions).
2. Identify layer-specific threats (use MAESTRO’s threat landscapes).
3. Hunt cross-layer threats (e.g., infra → data → model compromise paths).
4. Assess risk (likelihood/impact via a risk matrix).
5. Plan mitigations (layered controls + AI-specific defenses).
6. Implement & monitor (iterate as models, data, and threats evolve).

What MAESTRO Threat Modeling is Not

• Not a replacement for STRIDE/PASTA/etc. It extends/complements them with AI-specific threat classes, multi-agent context, and lifecycle emphasis.
• Not a one-time exercise. It assumes continuous monitoring, red-teaming, and adaptation as models and attacks evolve.

How MAESTRO Threat Modeling Enhances Agentic AI Security in IriusRisk

The main difference between MAESTRO and our current Agentic AI in IriusRisk is that MAESTRO focuses on how different AI agents work together and adds security in layers. While our current AI components already bring value, MAESTRO goes further by looking at how multiple agents interact with each other and their environment. This layered security approach gives a clearer picture of where vulnerabilities might appear.

Step-by-Step: Implementing MAESTRO Threat Modeling in IriusRisk

A multi-agent architecture consists of multiple agents that can scale or combine specialized roles and functions within an agentic solution. The following diagram shows an example of a multi-agent architecture in IriusRisk, enhanced with additional specialized roles and capabilities:

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If you open the component questionnaire for a component that (by default) belongs to the ML/AI IriusRisk category (an LLM Application component, in the image below):

You’ll see a new section for the MAESTRO threat modeling framework under the Assets questionnaire. Now, you should select which MAESTRO layers best describe the role and interactions of this component.:

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For this example (an LLM), the most relevant MAESTRO layers are the foundation model layer, which covers the core architecture and versions of the model; the data operations layer, where training, fine-tuning, and retrieval data introduce risks such as poisoning or leakage; the evaluation and observability layer, which ensures that new iterations are thoroughly tested and monitored for safety, robustness, and drift; and the security and compliance layer, which cuts across all others to provide governance, auditability, and regulatory alignment. In practice, Layers 1 and 2 define the model itself, Layer 5 secures its evolution, and Layer 6 enforces trust and accountability throughout the lifecycle.

Based on the selected responses, the right MAESTRO risk patterns are imported into the component. For example, for the Layer 1 (Foundation Models), we’ll find the following threats:

The threats are also mapped with the MITRE ATLAS framework techniques:

And the proposed countermeasures are mapped with the MITRE ATLAS framework mitigations:

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Conclusion: Benefits of MAESTRO Threat Modeling

With the addition of the MAESTRO framework, IriusRisk now extends its threat modeling capabilities to explicitly address agentic AI architectures, systems where multiple agents interact with each other, with tools, and with their environment. By mapping components to MAESTRO’s layered model, the platform brings in the right AI specific threats, risk patterns, and mitigations aligned with standards like MITRE ATLAS, so security teams can reason about agent behaviors, interactions, and vulnerabilities in a structured and repeatable way. This enhancement ensures that organizations can secure both traditional software and modern multi-agent ecosystems under a single consistent methodology, making threat modeling for agentic AI both accessible and actionable.

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References

• Agentic AI Threat Modeling Framework: MAESTRO | CSA
• https://www.aigl.blog/content/files/2025/04/Agentic-AI---Threats-and-Mitigations.pdf
• https://atlas.mitre.org/techniques
• https://atlas.mitre.org/mitigations

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