Your AI agent can read private data, follow untrusted instructions, and call external tools.That is how prompt injection becomes data exposure.

Helmwart turns your agent configuration into a threat model: what can read sensitive data, what accepts untrusted content, what can call out, and which connections create real findings. Use it before a customer review, audit, release gate, or incident forces the question.

Start a threat model →See the leak path →

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The review gap

Agent risk does not live in one prompt.

The dangerous part is the wiring. A model reads text you did not write. A tool gives it authority. Memory gives it sensitive context. An outbound API gives it somewhere to send data.

Import the system shape.Start from MCP, n8n, LangGraph, Bedrock, Foundry and other agent configs, then turn them into a graph.

Find the risky paths.See where agents, tools, memory, third-party content and outbound channels combine into findings.

Audits ask for evidence.Helmwart turns the model into findings, mitigations, and reports you can hand over.

The Helmwart canvas showing the MCP-based tool server template. The header counts 20 open findings, 2 trifecta findings, 2 zero-trust violations and 36 defence-in-depth gaps. Eight nodes sit in four trust zones: client agents, MCP server, external systems and supporting memory. The Analyst panel for Client Agent A reads: this agent can reach private data, untrusted content and an outbound network simultaneously; per Simon Willison and EchoLeak, CVE-2025-32711, this is the structural precondition that turns prompt injection into exfiltration; break any one leg to break the trifecta. — This is the pattern security teams miss: two client agents share one third-party tool server, creating thirteen connections and twenty findings in the shipped`mcp-tool-server` template.

Private data · untrusted content · outbound

This is the path that turns prompt injection into exfiltration.

This agent can reach private data, untrusted content, and an outbound network simultaneously. Per Simon Willison / EchoLeak (CVE-2025-32711), this is the structural precondition that turns prompt injection into exfiltration. Break any one leg to break the trifecta.
Analyst panel · Client Agent A · in the screenshot above

Not one of these is a problem by itself. The risk appears when the graph connects all three, and that is exactly the thing a normal review can miss.

P
Private data
Long-Term Memory persistent · sensitive
Both client agents read from it and write to it, so anything the model can be talked into repeating could have come from here.
U
Untrusted content
MCP Tool Server mcp · 3rd-party
A third party writes the tool descriptions and returns the results. Both arrive in the agent’s context as plain text, with nothing marking them as data rather than as instructions.
O
Outbound channel
External API 1 and 2 third-party
Client Agent A has no network access of its own. It reaches both of these through the tool server, and one of them accepts calls with no authentication.

Helmwart points to the node or edge that creates each leg, so closing the finding becomes an engineering decision instead of a vague security warning.

The question is coming

If you ship agents, someone will ask for this.

A customer security review. A SOC 2 control. An AI governance process. An internal release gate. A post-incident question.

Where is the threat model for this agent system?

Helmwart gives you the answer before that question arrives.

What you get

A threat model you can act on.

Fix the path

Each finding points to the node or connection that creates the risk, with a concrete mitigation to place.

Answer the review

Map findings to AI governance, security, and compliance requirements without rewriting the model by hand.

Gate the release

Run checks in CI so new critical agentic risks fail before they ship.

Product surfaces

One model. Security, governance, and release decisions from the same source.

The Helmwart template library: a grid of architecture templates including Blank canvas, Consumer Fintech and Personal RAG assistant, each card showing agent count, edge count, findings count and trifecta count. — **Start from a system that already has risk in it**Use one of 10 worked agent systems to see how the model behaves, or paste your own config when you are ready to check the real thing.

The Helmwart Atlas landing page: a connected threat map for agentic AI, showing 51 threats, 67 mitigations and 6 defensive playbooks, with quick references to the OWASP Agentic AI threat catalog, the OWASP Top 10 2026 ASI entries, and the MAESTRO L1-L7 layers. — **Explain the finding without starting from scratch**Every threat and every mitigation carries a full write-up, cross-referenced to OWASP, MAESTRO and MITRE ATLAS and sourced to the document it came from. When a finding needs to be understood rather than just closed, the material is already there.

A Helmwart threat entry: T6, Intent Breaking and Goal Manipulation, marked critical, tagged MAESTRO L3 Agent Frameworks and Cross-Layer, cross-referenced to ATLAS techniques AML.T0051, AML.T0051.001, AML.T0054 and AML.T0065, with a definition and three worked injection examples. — **Put the risk in front of a reviewer**Written so you can put it in front of a reviewer without doing homework first: what the threat is, how the attack actually runs, what it looks like while it is happening, and where the claim comes from.

The Helmwart compliance workspace with the Consumer Fintech sample assessed against NIST AI RMF 1.0. Control coverage reads 0 of 11 requirements, 119 gaps across 29 open findings. The four NIST sections Govern, Map, Measure and Manage each show their open counts, open findings by severity read 42 critical and 89 high, and the requirements list below is grouped by section. — **Turn the threat model into audit evidence**Your threat model restated as the control requirements an auditor asks about: NIST AI RMF, the EU AI Act, ISO 42001, the NHI Top 10. It runs on the model you already built and exports as evidence, so an audit stops being a questionnaire you fill in from memory.

Question 4 of the Helmwart threat-model wizard, Did we do a good enough job, with an expense-reimbursement system loaded. A posture score of 47 out of 100, grade F, reads Incomplete: the threat model does not yet meet the minimum quality bar. Beside it, system scope captured 60 percent, threats enumerated 100 percent, mitigations assigned 0 percent, and three optional assurance passes not yet run. An audit summary lists 49 threats surfaced with 49 residual open. — **Get past the blank-page problem**Most teams skip it because a blank methodology is a lot of work before it pays anything back. This asks four questions, enumerates the threats for you between them, and leaves you with the part that was always the point: the decisions.

Do not wait for the questionnaire.

Build the threat model now, while the system is still cheap to change.

Create a free account →How it works →

Helmwart is a threat-modeling tool, not a safety certificate. It shows the risks your agent graph creates, the controls that address them, and the evidence you can use to defend the decisions you made.