paranoid-cli 1.5.0

Open-source iterative threat modeling powered by LLMs - STRIDE + MAESTRO frameworks with DREAD scoring

Paranoid

Open-source, self-hosted, iterative threat modeling powered by LLMs.

Paranoid takes system descriptions (text, diagrams, or code via MCP) and produces comprehensive STRIDE + MAESTRO threat models through an LLM-powered pipeline with deterministic fallback. Configure 1–15 automated iteration passes, then review threats in a human-in-the-loop approve/reject cycle.

Features

• Zero Infrastructure: SQLite + sqlite-vec. One command to run: docker compose up [Coming Soon]
• Multi-Provider LLM: Anthropic, OpenAI, or Ollama (fully local/air-gapped)
• Dual Framework Support: STRIDE (traditional) + MAESTRO (AI/ML) auto-detected or run in parallel
• DREAD Risk Scoring: Automatic risk assessment with 5 dimensions (0-10 scale each, averaged) for severity classification
• Structured Input Templates: Tagged templates for component descriptions with assumption enforcement in the prompts
• Iterative Refinement: 1–15 configurable iteration passes with gap analysis
• Code-as-Input: Semantic code extraction via context-link MCP — --code /path/to/repo grounds threats in actual implementation
• Image-as-Input: Architecture diagram support via --diagram arch.png (vision API) or --diagram flow.mmd (Mermaid text)
• Deterministic Rule Engine: 362 curated patterns (STRIDE, MAESTRO, OWASP, MITRE ATT&CK, ATLAS, CAPEC, cloud misconfigurations) across 16 seed files — run alongside the LLM and merged into the final output
• Persistent Results: Every run is saved to SQLite automatically — inspect past models with paranoid models list / paranoid models show
• Export Formats: JSON (simple/full), SARIF (GitHub Security integration), Markdown (PRs / Confluence / Notion), PDF (security review sign-off)
• Post-Run Export: Re-export any saved model in any format with paranoid models export — run once, export many times
• Model Management: paranoid models delete removes a saved model and all its data; paranoid models prune bulk-deletes by age or status
• Pre-flight Gap Analysis: Description completeness check before running — warns about missing auth, trust boundaries, data flows, and external integrations; --strict exits CI with code 2 on error-severity gaps
• Edit Context Before Threats: Review and edit extracted assets, flows, and trust boundaries at /models/:id/context before or after a run; "Re-extract" re-runs only the extraction steps without regenerating threats
• DREAD Score Editing: Edit any threat's DREAD scores inline from the Review page — no page reload required
• Fast Provider Routing: Configurable FAST_MODEL uses a Haiku-class model for extraction and enrichment, reserving Sonnet/Opus for threat generation — cuts API cost without sacrificing quality
• Attack Tree & Test Case Enrichment: --enrich generates STRIDE/MAESTRO attack trees and Gherkin test cases per threat after the main pipeline run; included automatically in Markdown and PDF exports
• Editable Settings (Web UI): Runtime configuration editing at /settings — change provider, model, iterations, and more without restarting; protected by optional CONFIG_SECRET shared secret
• CI/CD Ready: CLI + GitHub Action with SARIF upload for automated threat detection

Quick Start

Step 1: Install

Choose the method that works best for you:

PyPI (Recommended):

pip install paranoid-cli
paranoid --version

Docker (self-hosted, web UI + CLI):

git clone https://github.com/theAstiv/paranoid && cd paranoid

# Configure your LLM provider
cp .env.example .env
# Edit .env — at minimum set ANTHROPIC_API_KEY (or OPENAI_API_KEY)

# Build and start (first build: ~5-10 min for Go + Node + Python stages)
docker compose up --build

# Web UI
open http://localhost:8000/app

# API docs (OpenAPI)
open http://localhost:8000/docs

See Running with Docker for build args, offline builds, and CLI usage inside the container.

Standalone Binary (No Python Required):

Download the pre-built binary for your platform from GitHub Releases:

Platform	Binary
Linux x86_64	paranoid-linux-x64
macOS ARM64 (Apple Silicon)	paranoid-macos-arm64
Windows x64	paranoid-windows-x64.exe

# Linux/macOS
chmod +x paranoid-linux-x64
./paranoid-linux-x64 --help

From Source (Development):

git clone https://github.com/theAstiv/paranoid
cd paranoid
pip install -e .

Step 2: Configure

Option A: Interactive Wizard (Recommended)

paranoid config init

# Follow prompts to configure:
#   - LLM Provider (Anthropic/OpenAI/Ollama)
#   - API Key
#   - Model name
#   - Default iterations

paranoid config show

Option B: Environment Variables

cp .env.example .env

Edit .env and add your provider configuration:

# Anthropic (Recommended)
ANTHROPIC_API_KEY=sk-ant-api03-xxxxxxxxx
DEFAULT_PROVIDER=anthropic
DEFAULT_MODEL=claude-sonnet-4-20250514

# OR OpenAI
OPENAI_API_KEY=sk-xxxxxxxxx
DEFAULT_PROVIDER=openai
DEFAULT_MODEL=gpt-4o

# OR Ollama (fully local, no API key)
OLLAMA_BASE_URL=http://localhost:11434
DEFAULT_PROVIDER=ollama
DEFAULT_MODEL=llama3

# Optional: path to context-link binary for --code flag
# If unset, Paranoid looks for bin/context-link then PATH
CONTEXT_LINK_BINARY=/usr/local/bin/context-link

# Optional: fast model for extraction/enrichment steps (Anthropic only)
# Defaults to claude-haiku-4-5-20251001; set to same value as DEFAULT_MODEL to disable fast routing
FAST_MODEL=claude-haiku-4-5-20251001

# Optional: shared secret to protect PATCH /config (web UI settings page)
# If set, the Settings page requires this value to save any configuration change
CONFIG_SECRET=your-secret-here

# Optional: restrict CORS origins (default: * allows all)
# Comma-separated for multiple origins
CORS_ORIGINS=https://app.example.com,https://staging.example.com

Step 3: Run Your First Threat Model

# Run with an example
paranoid run examples/stride-example-api-gateway.md

# With architecture diagram (Mermaid, PNG, or JPG)
paranoid run examples/stride-example-api-gateway.md \
  --diagram examples/stride-api-gateway-architecture.mmd

# With your own system description
paranoid run my-system.md

# See all options
paranoid run --help

Expected Output:

Configuration:
  Provider: anthropic
  Model: claude-sonnet-4-20250514
  Iterations: 3
  Framework: STRIDE

[>] summarize: Generating system summary...
[OK] summarize: Summary generated: 196 chars
[>] extract_assets: Identifying assets and entities...
[OK] extract_assets: Identified 14 assets/entities
[>] extract_flows: Extracting data flows and trust boundaries...
[OK] extract_flows: Identified 12 flows, 6 boundaries
[>] generate_threats [iter 1]: Generating threats (iteration 1/3)...
[OK] generate_threats [iter 1]: Generated 10 threats
...
[OK] complete: Pipeline complete: 2 iterations, 17 threats

================================================================================
THREAT MODEL COMPLETE
================================================================================
Total Threats:      17
Iterations:         2
Duration:           115.0 seconds
Output:             stride-example-api-gateway_threats.json

Expected Runtime:

• Claude Sonnet: ~30-60 seconds (3 iterations)
• GPT-4: ~45-90 seconds (3 iterations)
• Ollama (local): 2-5 minutes (depends on hardware)

CLI Reference

Running Threat Models

# Basic usage (auto-detects framework from input)
paranoid run system.md

# Structured templates (auto-detects STRIDE vs MAESTRO)
paranoid run examples/stride-example-api-gateway.md
paranoid run examples/maestro-example-rag-chatbot.md

# JSON output (simple format - lightweight, ~2-3 KB)
paranoid run system.md --output threats.json

# JSON output (full format - complete models + DREAD + events, ~45 KB)
paranoid run system.md --format full -o complete.json

# SARIF export for GitHub Security integration
paranoid run system.md --format sarif -o threats.sarif

# Markdown export for PRs, Confluence, and Notion
paranoid run system.md --format markdown -o threats.md

# Markdown export with auto-suffix (no extension needed)
paranoid run system.md --format markdown -o threats

# PDF export for security review sign-off and archival
paranoid run system.md --format pdf -o report.pdf

# PDF export with auto-suffix
paranoid run system.md --format pdf -o report

# Force dual framework (STRIDE + MAESTRO in parallel)
paranoid run system.md --maestro

# Override iteration count (1-15)
paranoid run system.md --iterations 7

# Override framework auto-detection
paranoid run system.md --framework MAESTRO

# Override provider and model for a single run (without changing config)
paranoid run system.md --provider openai --model gpt-4o
paranoid run system.md --provider anthropic --model claude-opus-4-5

# Quiet mode (suppress real-time output, show only summary)
paranoid run system.md --quiet

# Verbose mode (show detailed event data with complete models)
paranoid run system.md --verbose

# Code-as-input: ground threats in actual source code (requires context-link binary)
paranoid run system.md --code /path/to/repo

# Image-as-input: include architecture diagram (PNG/JPG via vision API)
paranoid run system.md --diagram architecture.png

# Image-as-input: Mermaid diagram as text (all providers)
paranoid run system.md --diagram flow.mmd

# Combined: description + diagram + code context
paranoid run system.md --diagram arch.png --code /path/to/repo

# Strict mode: exit code 2 if description has error-severity gaps (for CI gates)
paranoid run system.md --strict

# Gap warnings are always printed to stderr; --strict makes errors blocking
paranoid run system.md --strict --format sarif -o findings.sarif

# Generate attack trees + Gherkin test cases per threat after the pipeline run
# Requires Anthropic provider; uses FAST_MODEL (claude-haiku-4-5-20251001) if configured
paranoid run system.md --enrich

# Enrich and export to Markdown (attack trees + test cases included in output)
paranoid run system.md --enrich --format markdown -o enriched-report.md

# Enrich and export to PDF
paranoid run system.md --enrich --format pdf -o enriched-report.pdf

Inspecting Saved Models

Every paranoid run saves its results to SQLite automatically. Use the models subcommand to browse and inspect past runs without re-running the pipeline.

# List recent threat models (most recent first)
paranoid models list

# Limit results
paranoid models list --limit 50

# Machine-readable JSON output
paranoid models list --json

# Show threats for a saved model — partial ID works (first 8 chars)
paranoid models show a1b2c3d4

# Full UUID also accepted
paranoid models show a1b2c3d4-e5f6-7890-abcd-ef1234567890

# Show threats without mitigations
paranoid models show a1b2c3d4 --no-mitigations

# JSON output (model metadata + threats array)
paranoid models show a1b2c3d4 --json

# Export a saved model to Markdown (run once, export many times)
paranoid models export a1b2c3d4 --format markdown -o report.md

# Export to PDF for security review sign-off
paranoid models export a1b2c3d4 --format pdf -o report.pdf

# Export to SARIF for GitHub Security
paranoid models export a1b2c3d4 --format sarif -o findings.sarif

# Export to JSON (simple summary or full raw dump)
paranoid models export a1b2c3d4 --format simple -o threats.json
paranoid models export a1b2c3d4 --format full -o complete.json

# Default output path (no -o flag): {id_prefix}_{format}.{ext} in cwd
paranoid models export a1b2c3d4 --format markdown

# Delete a saved model and all its data (prompts for confirmation)
paranoid models delete a1b2c3d4

# Delete without prompt (for scripting/CI)
paranoid models delete a1b2c3d4 --yes

# Prune old models (older than 30 days)
paranoid models prune --older-than 30

# Prune all failed models without prompting
paranoid models prune --status failed --yes

# Combine filters: old AND failed
paranoid models prune --older-than 7 --status failed

Example models list output:

  ID          Title                 Framework  Threats  Status      Date
  --------------------------------------------------------------------------
  a1b2c3d4    api-gateway           STRIDE           23  completed   2026-04-01 14:32
  e5f6g7h8    auth-service          STRIDE           17  completed   2026-03-28 09:15
  c9d0e1f2    rag-chatbot           MAESTRO          31  completed   2026-03-25 11:04

Example models show output:

  Threat Model: api-gateway
  ============================================================
  ID:           a1b2c3d4-...
  Framework:    STRIDE
  Threats:      23
  Iterations:   3

  THREATS
  ------------------------------------------------------------
  [1] SQL Injection  (Tampering)  pending
      Target: PostgreSQL DB  |  Impact: High  |  Likelihood: Medium
      → Use parameterized queries
      → Apply input validation
  ...

Configuration Management

# Interactive setup wizard
paranoid config init

# Reconfigure (overwrite existing config)
paranoid config init --force

# Display current configuration
paranoid config show

# Config file location: ~/.paranoid/config.json

Version Info

# Show version, Python version, dependencies, and current configuration
paranoid version

Running with Docker

The Docker image bundles all three components — FastAPI backend, Svelte frontend, and the context-link MCP binary — into a single container. The frontend is served as static files from FastAPI at /app.

Standard build

git clone https://github.com/theAstiv/paranoid && cd paranoid
cp .env.example .env           # copy and edit with your API key
docker compose up --build      # builds all three stages, then starts the server

First build takes roughly 5–10 minutes (Go toolchain download + Node modules + Python dependencies + fastembed model pre-bake). Subsequent builds are fast due to Docker layer caching.

Open the web UI: http://localhost:8000/app
Open the API docs: http://localhost:8000/docs

Configuration

Set environment variables in .env (picked up automatically by docker compose):

# Minimum — set at least one provider key
ANTHROPIC_API_KEY=sk-ant-api03-xxx
DEFAULT_PROVIDER=anthropic
DEFAULT_MODEL=claude-sonnet-4-20250514

# Optional overrides (defaults shown)
DEFAULT_ITERATIONS=3
PORT=8000
LOG_LEVEL=info
CORS_ORIGINS=*
SIMILARITY_THRESHOLD=0.85

# Fast model for extraction/enrichment (Anthropic only, optional)
FAST_MODEL=claude-haiku-4-5-20251001

# Shared secret to protect the Settings PATCH /config endpoint (optional)
CONFIG_SECRET=your-secret-here

All variables are documented in .env.example.

Build arguments

Argument	Default	Purpose
CONTEXT_LINK_VERSION	1.0.0	context-link release to download from GitHub releases
EMBEDDING_MODEL	BAAI/bge-small-en-v1.5	fastembed model to pre-bake into the image

# Pin to a specific context-link release
CONTEXT_LINK_VERSION=1.0.0 docker compose build

# Build with a different embedding model
EMBEDDING_MODEL=BAAI/bge-base-en-v1.5 docker compose build

Building without context-link (offline / no Go toolchain)

If you can't reach GitHub at build time, remove the Go stage:

1. Comment out or delete the context-link-builder stage in Dockerfile.
2. Remove the COPY --from=context-link-builder line in the final stage.
3. Mount your own pre-built binary at runtime:

# docker-compose.yml — uncomment this volume entry
volumes:
  - ./data:/app/data
  - /path/to/context-link:/app/bin/context-link:ro

The app works without the binary — the --code flag just logs a warning and runs without code context.

Using the CLI inside the container

# Run a threat model against a file already inside the container
docker compose exec app paranoid run /app/examples/stride-example-api-gateway.md

# Mount a local file and run it
docker run --rm \
  -v $(pwd)/my-system.md:/workspace/system.md \
  -v $(pwd)/data:/app/data \
  -e ANTHROPIC_API_KEY=sk-ant-xxx \
  paranoid-app-1 \
  paranoid run /workspace/system.md --format markdown -o /app/data/out.md

Persistent data

The SQLite database is stored at /app/data/paranoid.db inside the container, bind-mounted to ./data/ on the host. This directory persists threat models across container restarts and image upgrades.

---

Output Formats

JSON Simple (default, ~2-3 KB)

Lightweight threat summaries for CI/CD dashboards and quick reviews.

{
  "execution": {
    "total_threats": 17,
    "iterations_completed": 2,
    "duration_seconds": 115.0
  },
  "threats": [
    {
      "name": "JWT Token Forgery",
      "category": "Spoofing",
      "target": "API Gateway",
      "impact": "Complete authentication bypass",
      "likelihood": "Medium",
      "mitigation_count": 3
    }
  ]
}

JSON Full (~45 KB)

Complete Pydantic models with DREAD scores and full pipeline event audit trail. Suitable for detailed analysis, archival, and integration with other tools.

paranoid run system.md --format full -o complete.json

Markdown (~4–15 KB)

Human-readable reports for PRs, Confluence, Notion, and security review documents. Contains a summary table, per-category threat sections, DREAD scores, and tagged mitigations.

paranoid run system.md --format markdown -o threats.md

Output structure:

# Threat Model: my-system

**Framework:** STRIDE | **Model ID:** `a1b2c3d4` | **Generated:** 2026-04-02

## Summary
| # | Threat | Category | Target | Likelihood | DREAD |
...

## Threats

### Tampering

#### 1. SQL Injection
**Target:** Database | **Likelihood:** High | **Impact:** Data breach
**DREAD:** 7.5/10 *(D:8 R:7 E:8 A:6 Di:7)*

> An attacker exploits unparameterized queries...

**Mitigations:**
- [P] Use parameterized queries / prepared statements
- [D] Enable query anomaly logging

Pass include_header=False when calling export_markdown() directly to omit the H1 heading and metadata block for embedding into existing documents.

PDF (~50–200 KB)

Structured PDF reports for security review sign-off, archival, and sharing with stakeholders who don't use Markdown. Contains the same content as the Markdown export: title, metadata, summary table, per-category threat sections, DREAD scores, and tagged mitigations. Produced via reportlab — no external binaries required.

paranoid run system.md --format pdf -o report.pdf
paranoid models export a1b2c3d4 --format pdf -o report.pdf

SARIF (GitHub Security Integration)

SARIF 2.1.0 format for GitHub Security tab, GitLab, VS Code, and Azure DevOps:

paranoid run system.md --format sarif -o threats.sarif

GitHub Actions Integration:

Use the official Paranoid action for zero-config SARIF upload to the GitHub Security tab:

name: Threat Model

on: [push, pull_request]

permissions:
  security-events: write
  contents: read

jobs:
  threat-model:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run Paranoid threat modeling
        id: paranoid
        uses: theAstiv/paranoid@v1.4.0
        with:
          description-file: docs/system-description.md
          provider: anthropic
          api-key: ${{ secrets.ANTHROPIC_API_KEY }}
          framework: STRIDE
          iterations: 3

      - name: Upload SARIF to GitHub Security tab
        uses: github/codeql-action/upload-sarif@v3
        if: always()
        with:
          sarif_file: ${{ steps.paranoid.outputs.sarif-file }}

Action inputs:

Input	Default	Description
description-file	—	Path to system description (.md / .txt), relative to repo root
provider	anthropic	anthropic, openai, or ollama
api-key	—	Provider API key; omit for Ollama
model	provider default	Model override (e.g. claude-sonnet-4-5, gpt-4o)
framework	STRIDE	STRIDE or MAESTRO
iterations	3	Pipeline iterations (1–15)
sarif-output	paranoid-results.sarif	SARIF output path
strict	false	Exit 2 on error-severity description gaps
fail-on-findings	false	Fail the step if any threats are found

Code-as-Input (--code)

Ground threats in actual source code using the context-link MCP binary. When --code is provided, Paranoid extracts a semantically relevant slice of the codebase and threads it through every pipeline node.

paranoid run system.md --code /path/to/repo
paranoid run system.md --code /path/to/repo --iterations 5
paranoid run system.md --diagram arch.png --code /path/to/repo  # combined

How it works:

1. Three-tier extraction funnel (50KB budget, ~12.5K tokens):

   • Semantic search: semantic_search_symbols finds symbols relevant to the threat model description
   • Code body extraction: get_code_by_symbol fetches full source for top results
   • File skeletons: get_file_skeleton fills remaining budget with structural outlines

2. Code summary step: _deterministic_code_summary() extracts a focused ~2KB CodeSummary (tech stack, entry points, auth patterns, data stores, security observations) from CodeContext using file-extension mapping, import scanning, and keyword matching — no LLM call required. summarize_code() (LLM-backed) is available as an opt-in upgrade path when pattern matching isn't sufficient.

3. Full pipeline threading: The CodeSummary is passed to all downstream nodes — extract_assets, extract_flows, generate_threats, and gap_analysis — so threats reference actual implementation details.

4. Deterministic extraction: _deterministic_code_summary() covers tech stack from file extensions, HTTP routes from decorator patterns, auth/DB/HTTP-client keywords, and security anti-patterns (eval(), pickle.load, shell=True, SQL string concatenation). The pipeline never silently drops code context.

Installing context-link:

context-link is a standalone Go binary that indexes your repository and serves an MCP tool interface over stdio. Get it from github.com/context-link-mcp/context-link:

# Option 1: pre-built binary (recommended — no Go toolchain required)
# Download the binary for your platform from the GitHub Releases page:
# https://github.com/context-link-mcp/context-link/releases/latest
# Then place it at bin/context-link or add it to PATH.

# Option 2: build from source (requires Go 1.22+)
git clone https://github.com/context-link-mcp/context-link
cd context-link
CGO_ENABLED=1 go build -o context-link ./cmd/context-link
# Move the resulting binary to bin/context-link or PATH

Requirements:

• context-link binary at bin/context-link (relative to working directory), on PATH, or at the path set by CONTEXT_LINK_BINARY
• Binary discovery order: explicit CONTEXT_LINK_BINARY env var → ./bin/context-link → shutil.which("context-link")
• If the binary is not found, Paranoid logs a warning and continues without code context

Error handling: Every MCP failure degrades gracefully — binary not found, subprocess crash, tool call error, and index timeout all produce a warning and allow the pipeline to continue with text-only input.

---

Code Sources (Web UI)

The web UI lets you clone and index Git repositories directly from the browser, then select a source when creating a new threat model — no CLI or Docker volume required.

Adding a code source

1. Navigate to Sources in the top navigation.
2. Click Add source and fill in:
   • Git URL — https://github.com/owner/repo.git (GitHub, GitLab, Bitbucket, or hosts added via ADDITIONAL_GIT_HOSTS)
   • Name — a short label shown in the wizard
   • Branch / tag / commit (optional) — leave blank to track the default branch; enter a commit SHA to pin to a specific revision
   • Personal access token (optional) — for private repos; stored Fernet-encrypted at rest
3. Click Add & clone. The source card shows live progress: queued → cloning → indexing → ready.

Once a source is ready, select it in the Code Source step of the New Model wizard. The pipeline extracts a semantically relevant slice of the codebase and threads it through every threat-generation step.

Security model for stored credentials

• PATs are Fernet-encrypted (AES-128-CBC + HMAC-SHA256) before being written to SQLite.
• Key derivation: CONFIG_SECRET env var → PBKDF2-HMAC-SHA256 (100k iterations); falls back to a random 32-byte file key at data/.source_key (mode 0600).
• The raw PAT is never logged, never included in SSE events, and never returned by any API endpoint (has_pat: bool only).
• Git clones use hardened flags: protocol.file.allow=never (blocks submodule file:// transport), core.symlinks=false, GIT_TERMINAL_PROMPT=0.

Requirements

• RAM: 4 GB minimum. The fastembed ONNX embedding model (~130 MB resident), context-link indexer, and a git clone of a large monorepo can peak at 2–3 GB together; leave headroom for the LLM pipeline.
• Disk: clone directories live at data/sources/<id>/ inside the mounted data volume.
• Network: HTTPS-only git URLs; host allowlist is github.com, gitlab.com, bitbucket.org by default.

# Allow additional private hosts (exact hostname match, no wildcards)
ADDITIONAL_GIT_HOSTS=git.company.com,git.internal.net docker compose up

---

Image-as-Input (--diagram)

Supply an architecture diagram alongside your text description. Paranoid passes it to every pipeline node for richer threat coverage.

# PNG or JPG via vision API
paranoid run system.md --diagram architecture.png
paranoid run system.md --diagram architecture.jpg

# Mermaid (.mmd) as text — works with all providers
paranoid run system.md --diagram flow.mmd

# Combined with code context
paranoid run system.md --diagram arch.png --code /path/to/repo

Supported formats:

Format	Mechanism	Size Limit
PNG	Vision content block (base64)	5MB
JPG/JPEG	Vision content block (base64)	5MB
Mermaid .mmd	<architecture_diagram> XML tag (text)	100KB

Provider support:

Provider	PNG/JPG	Mermaid
Anthropic (all models)	Full support	Full support
OpenAI gpt-4o, gpt-4o-mini	Full support	Full support
OpenAI other models	Not supported (use gpt-4o)	Full support
Ollama	Logs warning, continues without image	Full support

How it works:

• PNG/JPG: The image is base64-encoded and passed as a vision content block in the provider's native format (Anthropic image content block; OpenAI image_url data URI). Each prompt's <architecture_diagram> instruction is replaced with a vision-specific directive.
• Mermaid: The .mmd file is read as UTF-8 text and injected as <architecture_diagram> XML in the prompt. All providers parse Mermaid syntax natively — no rendering required.
• Pipeline threading: DiagramData threads through all 5 pipeline nodes: summarize, extract_assets, extract_flows, generate_threats, and gap_analysis.

---

Structured Input Templates

Paranoid supports rich XML-tagged templates for better context and assumption enforcement. See Input-template.md for the full template reference and examples/ for working examples.

STRIDE Template (traditional systems): Component description with technology stack, interfaces, data handling, and 6 structured assumption sections.

MAESTRO Template (AI/ML systems): Extended component description with mission alignment, agent profile, and 9 structured assumption sections.

Model Configuration

Use Claude Sonnet (or newer) for reliable structured output generation.

Model	Status	Notes
claude-sonnet-4-20250514	Recommended	Validated end-to-end, reliable for production
claude-haiku-4-5-20251001	Not recommended as main model	Fails with JSON parsing errors on complex threat outputs; recommended as FAST_MODEL for extraction/enrichment steps
gpt-4o	Supported	Works well, also supports vision (--diagram)
Ollama (Llama 3 70B+)	Supported	Fully local, no external API calls

Architecture

• Backend: FastAPI + SQLite + sqlite-vec (loaded via sqlite_vec.loadable_path() — works on Windows without manual DLL installation)
• Frontend: Svelte + Tailwind SPA with svelte-spa-router — implemented (see frontend/); available when running the web server (uvicorn backend.main:app)
• LLM Providers: Anthropic / OpenAI / Ollama (protocol-based, swappable)
• Pipeline: Plain async functions (no LangChain, no LangGraph)
• Embeddings: Local via fastembed (ONNX, BAAI/bge-small-en-v1.5)
• Models: Pydantic v2 for all data validation
• Frameworks: STRIDE (traditional) + MAESTRO (AI/ML security)
• Code context: context-link MCP binary (Go) + MCPCodeExtractor async context manager
• Image input: backend/image/ package — encoder.py (PNG/JPG base64), mermaid.py (text load), validation.py (size/format)

Python API:

from backend.pipeline.runner import run_pipeline_for_model
from backend.providers.anthropic import AnthropicProvider
from backend.models.enums import Framework

provider = AnthropicProvider(model="claude-sonnet-4-20250514", api_key="your-key")

async for event in run_pipeline_for_model(
    model_id="web-app-001",
    description="E-commerce web application...",
    framework=Framework.STRIDE,
    provider=provider,
    max_iterations=3,
):
    print(f"[{event.step}] {event.message}")

Testing

# Run unit/integration tests (no API key required)
pytest tests/ -v

# Run end-to-end pipeline test (requires ANTHROPIC_API_KEY)
pytest tests/test_pipeline_e2e.py -v

# Validate structured input parser (no API key required)
python examples/test_structured_input.py

# Lint
ruff check backend/ cli/
ruff format backend/ cli/

Troubleshooting

Authentication errors:

• Check your API key is correct in .env or ~/.paranoid/config.json
• For Anthropic, ensure billing is set up and key starts with sk-ant-api03-

Module not found:

pip install -e .  # or: pip install paranoid-cli

Ollama connection refused:

ollama serve  # start Ollama first, then run paranoid

--code flag: context-link binary not found:

Install context-link from github.com/context-link-mcp/context-link (pre-built binaries on the Releases page), then make it available to Paranoid:

# Option 1: set env var pointing to the binary
export CONTEXT_LINK_BINARY=/path/to/context-link

# Option 2: place binary at bin/context-link relative to working directory

# Option 3: add context-link to PATH

If the binary is missing, Paranoid logs a warning and continues without code context.

sqlite-vec not loading (Windows "module not found"):

This was a known issue on Windows where a bare vec0 extension name was used. It is fixed in the current release — the extension is now loaded via the absolute path from the bundled sqlite-vec Python package (sqlite_vec.loadable_path()), which resolves correctly on all platforms. No manual DLL installation required; pip install paranoid-cli includes everything.

--diagram with OpenAI and vision errors: Only gpt-4o and gpt-4o-mini support JSON structured output together with vision. Other OpenAI models that support vision do not support JSON mode simultaneously. Switch to gpt-4o or use Anthropic.

--diagram with Ollama: Ollama does not support vision for most models. Paranoid logs a warning and continues without the image. Use --diagram flow.mmd (Mermaid text) instead — all providers support Mermaid.

macOS binary "developer cannot be verified":

xattr -d com.apple.quarantine paranoid-macos-arm64

Windows "Windows protected your PC": Click "More info" then "Run anyway", or add an exception in Windows Defender.

Platform Support

Platform	PyPI	Docker	Binary
Ubuntu 20.04+	Yes	Yes	Yes
Debian 11+	Yes	Yes	Yes
RHEL 8+	Yes	Yes	Yes
macOS 11+ (Intel)	Yes	Yes	No
macOS 11+ (ARM64)	Yes	Yes	Yes
Windows 10+	Yes	Yes	Yes
WSL2	Yes	Yes	Yes

Documentation

• CHANGELOG.md — Release history
• Input-template.md — Structured input template reference
• examples/ — Working STRIDE and MAESTRO examples
• RELEASE.md — Maintainer release checklist

Developer docs:

• .claude/rules/CLAUDE.md — Project structure and conventions
• .claude/rules/RULES.md — Coding standards
• .claude/rules/tech-decision-rationale.md — Architecture decisions

Development Status

v1.5.0 — Docker UX release. Non-technical users can now open the web app, paste an API key, link a Git repo, and get a threat model — no terminal required.

Completed: Core pipeline (8 nodes, iteration logic, SSE, dual framework), LLM providers (Anthropic/OpenAI/Ollama), STRIDE + MAESTRO prompts, structured input templates, JSON + SARIF + Markdown + PDF export, DREAD scoring, CLI with config wizard, code-as-input via context-link MCP (--code CLI and code sources web UI), image-as-input via vision API and Mermaid text (--diagram), deterministic rule engine (362 curated patterns across 16 seed files, RAG retrieval), provider offline fallback (rule-engine-only mode), Anthropic prompt caching (~20–30% token reduction), full SQLite persistence (every run saved with assets/flows/threats/DREAD), full CRUD for all 12 schema entities, paranoid models list/show/export/delete/prune commands, --provider/--model run-time overrides, REST API (26+ routes with SSE and full CRUD), Svelte + Tailwind frontend (all pages and components implemented), Docker Compose deployment (3-stage build: Go + Node + Python, web UI served at /app), packaging and release automation, fast provider routing (FAST_MODEL for extraction/enrichment), --enrich CLI flag (attack trees + Gherkin test cases per threat), enrichment included in Markdown/PDF exports, encrypted API key + PAT storage (Fernet), first-run redirect + Settings UI overhaul, provider liveness probe, git clone + index pipeline with SSE progress, CSRF protection (ALLOWED_ORIGINS).

Future (v2.0+): Multi-user collaboration.

License

Apache 2.0