mastyf.ai

Perimeter security for your AI.

Runtime enforcement, policy control, and full audit trail for every AI action.

Website · Quick start · Policy · Dashboard · GitHub

The problem

AI agents can read your files, push code, query databases, execute shell commands, and call external APIs. They do it autonomously, at machine speed.

Traditional security controls weren't built for that.

Mastyf.ai acts as a perimeter security layer for AI. It intercepts every tool call, evaluates it against your security policies using multi-agent swarm analysis, and blocks malicious or unauthorized actions before they execute.

Every decision is enforced, logged, and auditable.

What it stops

Quick start

Build from source

Clone the repository and run the setup script.

Requirements:

• Git
• Linux (the setup script installs Nix automatically if needed)

git clone https://github.com/mastyf-ai/mastyf.ai.git
cd mastyf.ai
chmod +x setup.sh
./setup.sh

The setup script automatically:

• Installs Nix (if required)
• Enables Nix flakes
• Creates the development environment
• Installs all project dependencies
• Rebuilds native packages
• Builds the entire project
• Adds a convenient mastyf shell alias

Once installation completes, start the proxy and dashboard:

node dist/cli.js start

Or simply use the alias after opening a new terminal:

mastyf

The dashboard will be available at:

• Dashboard: http://localhost:4000

Test the installation

If the dashboard is running, verify the HTTP bridge:

curl -X POST http://localhost:4000/mcp -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":"1","method":"tools/list","params":{}}'

Dashboard

Full visibility into every action your AI takes.

Do not expose port 4000 publicly without enabling dashboard auth. The default local dev config has DASHBOARD_AUTH_DISABLED=true.

How enforcement works

Every tool call passes through three layers before it reaches your infrastructure.

Layer 1 - Pattern detection
Regex-based scanning for injection, dangerous paths, leaked secrets, shell commands, and encoding tricks. Runs in microseconds with no external dependencies.

Layer 2 - Schema validation
Rejects malformed payloads, oversized arguments, and JSON-RPC violations before they reach policy evaluation.

Layer 3 - Semantic review
An optional local LLM (Ollama) or cloud model evaluates borderline calls that pass pattern checks. Falls back to heuristics if no model is configured.

Anything that fails is blocked. The tool never runs. Everything is logged.

Policy

Your rules live in default-policy.yaml. You own them. mastyf.ai enforces them.

policy:
  mode: block
  default_action: pass
  unicode_strict: true
  rules:
    - name: block-sensitive-paths
      action: block
      argPatterns:
        - field: path
          patterns: ['^/etc/', '/\.ssh', '/\.aws/credentials']

    - name: rate-limit-tool-calls
      action: block
      maxCallsPerMinute: 120

    - name: block-shell-injection
      action: block
      patterns: ['rm\s+-rf', 'curl\s', 'wget\s', '`[^`]+`']

Roll out safely with three enforcement modes:

Pre-built templates for HIPAA, PCI-DSS, GxP, and data residency are in policy-templates/.

Architecture

mastyf.ai runs two coordinated swarms. The CI Swarm attacks your policy before code ships. The Runtime Swarm enforces and learns from every live tool call in production. Four feedback loops connect them so the system gets harder to bypass over time.

flowchart TB

  AI["🤖 AI Clients\nCursor · Claude Desktop · Cline"]

  subgraph CI["🔵 CI Swarm (PR + Nightly)"]
    direction LR
    Scout["🔍 Scout Agent\nSAST, deps, config scan"]
    Corpus["📋 Corpus Agent\n228 fixtures eval"]
    Evasion["⚡ Evasion Agent\n120+ probes + generate new"]
    Parity["🔄 Parity Agent\nNode vs Python"]
    ProxyA["🖥️ Proxy Agent\nLive stdio MCP tests"]
    Report["📊 Report Agent\nsecurity-swarm/latest.json"]
    Scout --> Corpus --> Evasion --> Parity --> ProxyA --> Report
  end

  subgraph Runtime["🟢 Runtime Swarm (Production Proxy)"]
    direction LR
    BG["🛡️ BlockGuard\nsync policy"]
    IL["📈 InstantLearner\nper-block stats + suggestions"]
    SA["🧠 SemanticAuditor\nasync LLM, optional"]
    PS["🔗 PatternSynthesizer\nbatch suggestions"]
    Cal["⚙️ Calibrator\nlabels + thresholds"]
    BG --> IL --> PS --> Cal
    BG --> SA --> PS
  end

  Tools["🗄️ MCP Tools\nfilesystem · GitHub · databases · APIs"]

  AI -->|"every tool call"| BG
  BG -->|"✅ allowed"| Tools
  Report -->|"🔁 Loop A: bypasses to corpus"| Corpus
  Cal -->|"🔁 Loop B: blocks to rules"| BG
  Cal -->|"🔁 Loop C: labels to LLM"| SA
  Report -->|"🔁 Loop D: CI metrics weekly"| Cal

  style CI fill:#EFF6FF,stroke:#3B82F6,stroke-width:2px,color:#1E3A5F
  style Runtime fill:#F0FDF4,stroke:#22C55E,stroke-width:2px,color:#14532D
  style Scout fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style Corpus fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style Evasion fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style Parity fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style ProxyA fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style Report fill:#DBEAFE,stroke:#3B82F6,color:#1E40AF
  style BG fill:#BBF7D0,stroke:#16A34A,color:#14532D
  style IL fill:#BBF7D0,stroke:#16A34A,color:#14532D
  style SA fill:#BBF7D0,stroke:#16A34A,color:#14532D
  style PS fill:#BBF7D0,stroke:#16A34A,color:#14532D
  style Cal fill:#BBF7D0,stroke:#16A34A,color:#14532D
  style AI fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#78350F
  style Tools fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#78350F

Canonical gates: 228/228 corpus, 0 bypasses, 100% parity

CI Swarm

Runs on every PR and nightly. Six agents work in sequence, each one hardening what the previous found.

Runtime Swarm

Runs inside the production proxy on every tool call.

Feedback loops

The proxy supports five transports: stdio, HTTP, SSE, streamable HTTP, and WebSocket.

For enterprise deployments with Redis, Postgres, and Kubernetes see docs/ENTERPRISE_DEPLOYMENT.md.

Threat Lab

Threat Lab watches live traffic and uses a local LLM to propose new attack test cases when it detects suspicious patterns. Nothing is applied automatically. You review and approve every suggestion in the dashboard before it becomes a rule.

Approved discoveries feed back into the CI attack corpus for ongoing regression testing.

ollama serve
ollama pull qwen3:8b

export OLLAMA_BASE_URL=http://127.0.0.1:11434
export MASTYF_AI_LLM_PROVIDER=ollama
export MASTYF_AI_LLM_MODEL=qwen3:8b

pnpm dashboard:proxy

MCP package trust scores

Before installing any MCP server from npm, check its trust score at https://www.mastyf.ai/certified. Scores cover CVE exposure, typo-squat risk, maintainer signals, and known attack patterns. Free, no account required.

Common commands

Troubleshooting

Learn more

• Enterprise deployment (Redis, Postgres, Helm)
• Defense pipeline in depth
• Security Swarm and CI red teaming
• Real-world MCP integration examples
• Core detection engine