Open source · AI agent security

Your model proposes.Mastyf decides.

Your AI can reason. Mastyf controls what it can execute: every tool call is checked against your policy, and unsafe ones are stopped before they run.

Layer
MCP tool calls
Decision
Under 4.8 µs
License
AGPL-3.0

Live demo — simulated

See what changes with Mastyf

Run the same agent task with and without Mastyf, or write your own tool call and watch the decision.

Simulated
Without Mastyf
  1. Press Run to replay this task.
Waiting
With Mastyf
  1. Press Run to replay this task.
Waiting

How it works

One boundary between the model and your systems

How enforcement works
  1. 01

    Your agent proposes

    The model decides to call a tool: read a file, run a command, send a request.

  2. 02

    Mastyf decides

    The call is checked against your policy before dispatch. The model cannot override the decision.

  3. 03

    Your tools execute

    Only approved calls reach your files, shell, and network. Blocked calls send zero bytes.

Overview — 1:32

Mastyf in ninety seconds

How agents act on your systems, what goes wrong without a boundary, and where Mastyf sits.

Captions are on screen, so it works with sound off.

0:00 / 0:00
Transcript

AI agents don't just answer questions. They take actions: reading files, changing code, calling APIs. Mastyf decides which of those actions actually happen.

An agent works in a loop. The model reads your request, chooses a tool, and that tool runs on your real systems: your files, your code, your data.

Without a boundary, whatever the model proposes runs. If untrusted content steers the model off course, an action outside your policy executes as easily as a safe one. And nothing records why.

With Mastyf in place, every tool call stops at the boundary first. It's checked against your policy: pattern rules, schema validation and an optional semantic review. Calls within policy pass straight through. Calls outside it are blocked before they ever reach the tool.

Mastyf sits outside the model's reasoning loop. It doesn't ask the model to police itself, and it doesn't depend on any one vendor. Working at the tool-call layer, one policy protects agents built on any model, in any client. Change your model or framework, and your policy stays the same.

Every decision is written to a tamper-evident audit log, so you can always show what your agent tried to do, and what was stopped.

Your model proposes. Mastyf decides.

Platform — five parts

One policy, wherever your agents run

Compare the components
  1. 01ShieldDesktop app that guards local agents like Claude Desktop and Cursor.macOSWindowsLinux
  2. 02GatewayFail-closed proxy for agents running in production.MCP stdio & HTTPAudit or enforce
  3. 03SwarmAttack tests for your agents, run in CI.GitHub ActionsRegression gates
  4. 04TrustSecurity scores for the MCP packages your agents install.Trust scoresCVE index
  5. 05Control PlaneFleet-wide policy, signed audit trails, and SSO.SSOAudit trailsSIEM export

Evidence — public benchmarks

Measured, with the method published

Benchmarks and methodologyRead the paper
Attack defense, AgentDojo
99.52%
Attack defense, InjecAgent
98.43%
Deterministic decision
<4.8µs
Reach a tool on block
0 bytes

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