MAESTRO (CSA Agentic AI Threat Modeling Framework)

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Summary: Source page for the Cloud Security Alliance’s MAESTRO framework — a layered threat-modelling methodology for agentic AI, built on the argument that all six mainstream threat-modelling frameworks fail on agents in the same four places.

Sources: raw/Agentic/Agentic AI Threat Modeling Framework_ MAESTRO.md

Last updated: 2026-07-30

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Provenance

Published 2025-02-06 on the Cloud Security Alliance blog, written by Ken Huang (CEO & Chief AI Officer, DistributedApps.ai; Co-Chair of the CSA AI Safety Working Groups; core contributor to the OWASP Top 10 for LLM Applications) (source: Agentic AI Threat Modeling Framework_ MAESTRO.md).

MAESTRO stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome. The acronym is expanded inconsistently in the source itself — the description field and section 3 give “Threat Risk” without the comma, the abstract gives “Threat, Risk” — a cosmetic defect, noted here so quotations can be checked.

This closes a gap the OWASP ASI document left open: OWASP names MAESTRO as a methodology practitioners should evaluate but does not describe it, and explicitly declines to use it in favour of its own component model.

One author, both frameworks. Huang contributes to the OWASP Top 10 and authored MAESTRO. The two decompositions are therefore not independent confirmations of each other — see maestro-asi-crosswalk.

What it contains

Section	Content	Wiki page
1–2	Six mainstream frameworks assessed against agentic AI, each with strengths, weaknesses, “specific AI gaps”, applicability	threat-modeling-frameworks
2.7	The four gap categories common to all six	threat-modeling-frameworks
3.1	Six design principles	below
3.2 A	Seven-layer reference architecture, each with its own threat landscape (~55 named threats)	maestro-seven-layers
3.2 B	Five cross-layer threats	maestro-seven-layers
3.2 C	Layer-specific, cross-layer and AI-specific mitigations	maestro-seven-layers
3.2 D	Six-step application method	below
4	Eight agentic architecture patterns, one canonical threat each	maestro-asi-crosswalk

The six principles

1. Extended security categories — STRIDE, PASTA and LINDDUN categories widened with AI-specific considerations.
2. Multi-agent and environment focus — agent-to-agent and agent-to-environment interaction modelled explicitly.
3. Layered security — “security isn’t a single layer, but a property that must be built into each layer of the agentic architecture.”
4. AI-specific threats — adversarial ML and autonomy risks as first-class categories.
5. Risk-based approach — prioritise by likelihood and impact within the agent’s context.
6. Continuous monitoring and adaptation — the threat model is re-run as the system evolves, “not a one time fix, but an iterative process.”

Principle 1 is the framework’s weakest point as written: the source introduces it with “For example:” and then supplies no example, moving straight to principle 2 (source: Agentic AI Threat Modeling Framework_ MAESTRO.md, §3.1). The promised extension of STRIDE/PASTA/LINDDUN categories is never delivered anywhere in the document.

The six-step method

1. System decomposition — map the system onto the seven layers; define agent capabilities, goals and interactions.
2. Layer-specific threat modelling — pull from each layer’s threat landscape, tailored to the system.
3. Cross-layer threat identification — analyse inter-layer interactions.
4. Risk assessment — likelihood × impact, prioritise.
5. Mitigation planning — layer-specific, cross-layer and AI-specific controls.
6. Implementation and monitoring — continuous, with threat-model updates.

Step 4 references “the risk measurement and risk matrix” as though defined earlier in the document. No risk matrix or measurement scheme appears anywhere in the source. The step that turns ~60 threats into a priority order is the one piece MAESTRO does not supply — which matters, because a flat list of 55 layer threats plus 5 cross-layer threats is not actionable without it.

Assessment

What it does better than anything else in the vault:

• Security tooling as attack surface. Layer 6 assumes AI agents are also used as security tools, and models attacks on them — evasion of security agents, poisoning their operational data, extracting their models to learn how to bypass them. Nothing else in the corpus treats the defence itself as a target. See maestro-seven-layers.
• Observability as attack surface. Layer 5 names poisoning of observability data and compromise of monitoring tools to hide malicious behaviour. The natural pairing is agent-observability-and-logging, which builds the audit trail MAESTRO’s Layer 5 tells you an attacker will target.
• Infrastructure layers taken seriously. Layers 3 and 4 (frameworks, deployment) carry conventional appsec threats — Kubernetes orchestration attacks, IaC tampering, container image compromise, lateral movement. OWASP ASI’s component model has no equivalent depth here.

What it does worse:

• No risk matrix, despite the method depending on one (above).
• Threat lists, not threat analysis. Most entries are one-sentence definitions. There are no attack scenarios of the kind ASI provides, no mitigation mapping per threat (mitigations are given once, globally), and no worked example.
• Formatting defects throughout. The per-layer threat lists are nested inconsistently — the first item of each list sits at one indent level and every subsequent item at another, in all seven layers. This is a converter artefact, but it makes the lists read as though the first threat were a parent of the rest. It is not.
• Undated internally. The clipping’s published frontmatter field is empty; only the body line “Published 02/06/2025” fixes the date.
• One embedded figure lost. Figure 1, the seven-layer mindmap, is a ~127,000-character base64 image in the clipping. The layer names and descriptions are recoverable from the body text; the mindmap’s internal structure is not. The source also links an external elaboration of the architecture on Medium, which is not held in raw/.

Related pages

• maestro-seven-layers — the layers and their threats
• threat-modeling-frameworks — why STRIDE et al. were judged insufficient
• maestro-asi-crosswalk — MAESTRO against the OWASP ASI model
• agentic-threat-taxonomy — the T1–T17 taxonomy MAESTRO’s threats map onto
• agent-observability-and-logging — implementation of the audit trail Layer 5 protects
• Mitigation frameworks — where MAESTRO’s control set sits against the others