pyobfus — the Python obfuscator

pyobfus (pronounced as "Python obfuscator") is a modern, AST-based python-obfuscator / code-obfuscator for developers who need to obfuscate before shipping while keeping failures diagnosable. Framework-aware presets, reverse stack-trace mapping, and a machine-readable JSON CLI let Claude Code, Cursor, GitHub Copilot, Codex, CodeBuddy, and any MCP-compatible AI agent help debug obfuscated stack traces. A transparent, open-source alternative to PyArmor.

A Python code obfuscator built with AST-based transformations. Supports Python 3.9 through 3.14. Provides reliable name mangling, string encoding, control-flow flattening, AES-256 string encryption, and — unique to pyobfus — a reverse-mapping workflow that lets you (or your AI coding assistant) debug obfuscated stack traces without giving up the protection.

🔒 Pro Edition available — 6 patent-targeted protection mechanisms (Selective Opacity, forensic watermarking, Runtime String Vault, and more) layered on top of the free AST obfuscator, $45 one-time, no subscription. See Pro Edition below.

🔎 What's new in v0.5.22 — pyobfus --check now emits a Python 3.14 remote-debug hardening advisory when a build both requests anti-debug protection and targets Python 3.14+: PEP 768 remote debugging can only be disabled at interpreter startup (-X disable_remote_debug / PYTHON_DISABLE_REMOTE_DEBUG=1), and the obfuscator's runtime anti-debug heuristics cannot switch it off. Informational only — detection, severity and exit codes are unchanged.

🔔 Starring this repo doesn't notify you about new releases — GitHub only sends release notifications to people who explicitly Watch it. Click Watch → Custom → Releases (top of this page) to get a heads-up the moment a new version ships, without the noise of every commit/issue.

🔌 Companion MCP server: pyobfus-mcp

This repository ships two installable packages:

The MCP server lives in pyobfus_mcp/ and is built on the official Model Context Protocol Python SDK (FastMCP). It registers eight MCP tools so Claude Desktop, Claude Code, Cursor, Windsurf, Zed, and Codex can call pyobfus directly from agent conversations — no shelling out:

The server is registered in the official MCP Registry under io.github.zhurong2020/pyobfus-mcp. The transport is stdio. See pyobfus_mcp/README.md for per-client configuration snippets.

🧩 Claude Code skill / plugin

This repo is also a Claude Code plugin marketplace. The pyobfus-protect skill teaches an agent the full "protect Python before shipping — obfuscate and verify it still runs" workflow (MCP-first, CLI fallback):

/plugin marketplace add zhurong2020/pyobfus
/plugin install pyobfus@pyobfus

See skills/ for the skill and install details. (This is distinct from templates/ai-integration/, which are copy-in rule files for your project.)

🧑‍💻 VS Code extension

pyobfus is also on the VS Code Marketplace and Open VSX (publisher zhurong2020, same version on both — Open VSX covers VSCodium, Gitpod, Eclipse Theia and code-server) — the first obfuscation-focused extension in this category, since no competitor (PyArmor, Nuitka, Sourcedefender) has one. Inline obfuscation-risk diagnostics (pyobfus --check findings rendered via VS Code's native DiagnosticCollection API — squiggles + Problems panel, no separate linter to configure), a "Reverse Stack Trace" command, a status bar item showing your current tier with a one-click menu (Check Workspace / Generate Config / Start Trial / Unlock Pro), a "Generate pyobfus.yaml" command, and right-click "Obfuscate with pyobfus" from the Explorer or editor. Source and design rationale in vscode-extension/ and docs/VSCODE_EXTENSION_PLAN.md.

🤖 AI-native features

• pyobfus --check src/ — config-aware pre-flight risk scan: detects eval/exec, dynamic attribute access, framework reflection points, and declared dependencies that do not exist on public PyPI before you obfuscate. It honors the same explicit/discovered config and presets as a build; findings from excluded files are reported separately without affecting the primary result. Use --no-config for the legacy unfiltered scan and --offline to skip PyPI lookups. JSON includes effective_config, excluded_findings, and an ai_hint telling your AI assistant what to run next. Add --sarif pyobfus.sarif to also emit a SARIF 2.1.0 report for GitHub Code Scanning (see docs/SARIF_CODE_SCANNING.md).
• pyobfus --init src/ — zero-config onboarding: scans the project, detects FastAPI/Django/Pydantic/Click/SQLAlchemy, and writes a ready-to-use pyobfus.yaml.
• pyobfus --unmap --trace error.log --mapping mapping.json — reverse obfuscated identifiers in a production stack trace so you can debug (or hand the trace to an AI assistant) without reversing the obfuscation itself.
• pyobfus … --save-mapping mapping.json --trace-marker — stamp each obfuscated file with a # pyobfus:obfuscated header (id + mapping filename + the exact --unmap command) so an AI agent that lands in an obfuscated file from a traceback immediately knows it's pyobfus output and how to reverse the names.
• pyobfus … --provenance-manifest provenance.json — write a local JSON manifest (input/output hashes, config hash, pyobfus version, git commit when available, mapping digest, CycloneDX-compatible component relationships, and a self-consistency integrity digest — not a cryptographic signature) for offline build provenance. See docs/PROVENANCE_MANIFEST.md.
• pyobfus --verify-provenance-manifest provenance.json --json — validate the manifest structure, CycloneDX-compatible relationships, and local integrity digest before archiving or shipping it.
• pyobfus … --dry-run --json — preview a versioned plan object before anything is written: the effective configuration, which files are selected or excluded (and why), and the artifacts a build would produce, each tagged ship / retain-internal / optional. Relative labels only (no source, secrets, or absolute paths); it is a preview, not a saved apply file.
• pyobfus … --verify-syntax — opt-in post-build check: compiles every generated .py in memory (no import, no execution, no __pycache__) and reports syntax_valid in JSON. A failure blocks delivery; it makes no runtime-correctness claim.
• Release provenance — pyobfus and pyobfus-mcp are published through PyPI Trusted Publishing with PEP 740 attestations; see docs/RELEASE_PROVENANCE_VERIFICATION.md for verification commands and the current snapshot.
• Framework-aware presets — --preset fastapi | django | flask | pydantic | click | sqlalchemy | ml with built-in exclusions for dispatch methods, decorators, ORM fields, migrations, model-serving wrappers, and dependency-injection parameters.
• Compatibility cookbooks — pair pyobfus with real delivery pipelines: import-hook / encrypted-file (SOURCEdefender .pye), compiled packaging (Nuitka / Cython), and ML model-serving. pyobfus --check also emits compatibility_advisory findings for these. See docs/IMPORT_HOOK_COOKBOOK.md, docs/COMPILED_PACKAGING_COOKBOOK.md, and docs/MODEL_SERVING_COOKBOOK.md. For a hardened Python 3.14+ deployment that uses anti-debug protection, --check also flags PEP 768 remote-debug exposure (which must be disabled at interpreter startup, not by the obfuscator) — see docs/REMOTE_DEBUG_HARDENING.md.
• Global --json — every CLI mode (obfuscate, --check, --unmap, --init) emits the same structured schema with an ai_hint field, ready for Claude Code, Cursor, Windsurf, and MCP servers to consume.

Features

✅ Free Edition

The following features are fully implemented and available in the current version:

• Cross-File Obfuscation: Consistent name obfuscation across multiple files

  • Automatic import statement rewriting
  • __all__ list updates with obfuscated names
  • Global symbol table with collision detection
  • Two-phase obfuscation pipeline (Scan → Transform)
  • Preview mode with --dry-run flag

• Name Mangling: Rename variables, functions, classes, and class attributes to obfuscated names (I0, I1, I2...)

• Comment Removal: Strip comments and docstrings

• String Encoding: Base64 encoding for string literals with automatic decoder injection

• Numeric / Constant Obfuscation (--numeric-obfuscation): replace integer and float literals with value-preserving opaque expressions (int → XOR/add/sub identities, float → float.fromhex) so the original constants no longer appear in the shipped source

• AI Provenance Stripping (--strip-ai-artifacts): remove AI-generation markers (e.g. Generated by Claude, Co-Authored-By: Claude) from docstrings and attribution dunders, so AI-assisted code doesn't ship with "this was AI-generated" fingerprints

• Incremental Builds (--incremental): skip a directory rebuild when every input file and the config are unchanged since the last successful build (cache at <output>/.pyobfus-cache/), useful in CI pipelines that cache artifacts

• Parameter Preservation: Preserve function parameter names for keyword argument compatibility (--preserve-param-names)

• Multi-file Support: Obfuscate entire projects with preserved import relationships

• File Filtering: Exclude files using glob patterns (test files, config files, etc.)

• Configuration Files: YAML-based configuration for repeatable builds

• Selective Obfuscation: Preserve specific names (builtins, magic methods, custom exclusions)

• Configuration Presets: --preset safe | balanced | aggressive for quick obfuscation-strength tradeoffs, plus framework-aware presets — --preset fastapi | django | flask | pydantic | click | sqlalchemy | ml — with built-in exclusions for dispatch methods, decorators, ORM fields, migrations, and dependency-injection parameters. --list-presets shows them all

• Pre-flight Risk Scanning (--check): detects eval/exec, dynamic attribute access, and framework reflection points before you obfuscate; add --sarif PATH to export findings as SARIF 2.1.0 for GitHub Code Scanning

• Reverse Stack-Trace Mapping (--unmap): reverse obfuscated identifiers in a production stack trace, so you (or an AI coding assistant) can debug without un-obfuscating the shipped code

• Build Provenance (--provenance-manifest, v0.5.5+): local JSON manifest of an obfuscation run — input/output file hashes, config hash, pyobfus version, git commit when available, mapping digest, and CycloneDX-compatible component relationships — for offline build provenance, no network calls

• Provenance Validation (--verify-provenance-manifest): validates manifest shape, CycloneDX-compatible relationships, and the local integrity digest; JSON output is available for CI/agent use

• Structured Dry-Run Plan (--dry-run --json, v0.5.19+): versioned plan object — effective config, selected/excluded files with reasons, and artifacts tagged ship / retain-internal / optional; relative labels only, preview-only (not applyable)

• Syntax-Only Output Verification (--verify-syntax, v0.5.19+): after a build, compiles generated Python in memory — no import, no execution, no __pycache__ — and reports syntax_valid in JSON; a failure blocks delivery and it makes no runtime-correctness claim

• Release Attestations: PyPI Integrity API / PEP 740 runbook for verifying pyobfus and pyobfus-mcp release artifacts

🔒 Pro Edition

The following advanced features are available with a Pro license:

• String Encryption

  • AES-256 encryption for strings
  • Runtime decryption with injected decoder
  • Automatic key generation

• Anti-Debugging

  • Debugger detection checks injected into functions
  • Four detection methods (v0.5.11): sys.gettrace() (Python-level tracers/debuggers), TracerPid via /proc/self/status (native debuggers on Linux — gdb, strace), WinAPI IsDebuggerPresent() (native debuggers on Windows), and a timing-skew check (catches single-stepping regardless of platform)
  • Default OFF to protect AI-debuggability; opt-in via --anti-debug
  • Heuristic, not a security boundary — documented in the CHANGELOG

• Control Flow Flattening

  • State machine transformation for if/else/elif
  • For/while loop flattening
  • Nested structure support
  • CLI: --control-flow

• Dead Code Injection

  • Insertion of unreachable code paths
  • Four strategies: after-return, false branches, opaque predicates, decoy functions
  • CLI: --dead-code

• License Embedding

  • Embed expiration dates: --expire 2025-12-31
  • Machine binding: --bind-machine
  • Run count limits: --max-runs 100
  • Offline verification - no external dependencies

• Runtime Policy (v0.5.9)

  • Refuse to import outside a build-time platform allowlist — a pure-Python generalization of PyArmor BCC's platform restrictions
  • OS allowlist: --requires-os Linux,Darwin
  • Minimum Python version: --requires-python-min 3.10
  • CPU architecture allowlist: --requires-arch x86_64,arm64
  • Any combination composes; each check is independent

• Embedded Encrypted Data (v0.5.10)

  • AES-256-GCM encrypt a resource file at build time and embed it base85-encoded in the output — closes the Nuitka Commercial "Protect Data Files" / PyArmor --bind-data gap
  • CLI: --embed-data path/to/resource.bin
  • Generates a get_embedded_data() accessor that decrypts on call, not at import

• Configuration Presets

  • --preset trial - 30-day time-limited version
  • --preset commercial - Maximum protection with machine binding
  • --preset library - For pip-distributable libraries
  • --preset maximum - Highest security with all protections
  • --list-presets - View all presets

Patent-targeted mechanisms (CN 202610712171X, introduced v0.5.0)

Six mechanisms, available both as the pyobfus_pro API and — as of v0.5.1 — as opt-in pyobfus build flags (single-file / --no-cross-file mode): --selective-opacity, --seal-code, --vault, --scrub-traceback, --fingerprint <buyer-id>, --expire-hard <date>. v0.5.3 adds --period <N> (run-counter limit), --opacity-config <opacity.toml> (pattern-driven L3 encryption by original qualname), and --bind-device / --bind-device-id <id> (device-locked L3 encryption). v0.5.4 extends --bind-device to Runtime String Vault keys too — previously only the Selective Opacity L3 layer was device-locked, so vault secrets decrypted on any machine; now each vault key is independently re-derived at runtime from the bound device.

• Selective Opacity — per-symbol protection layers (transparent / ai-readable / obfuscated / AES-256-GCM encrypted with lazy __code__ materialization).
• Forensic watermarking — per-buyer deterministic key derivation for piracy traceback.
• License binding combo — device / expiry / run-count binding woven into the AES-GCM decryption path (no separate patchable license check).
• @seal_code — build-time bytecode integrity hash; runtime in-memory-patch detection.
• --scrub-traceback — production traceback encryption (RSA-2048 + AES-256-GCM); reverse error IDs with the new pyobfus-unscrub CLI.
• Runtime String Vault — encrypted KV namespace for runtime secrets with lazy per-entry decryption.

Requires Python ≥ 3.9 as of v0.5.0 (3.8 dropped, EOL 2024-10).

See CURRENT_PLAN_ZH.md for the current project plan and priorities.

Try Pro Features FREE

Try all Pro features for 5 days - no registration or credit card required!

# Start your free trial
pyobfus-trial start

# Check trial status
pyobfus-trial status

# Use Pro features during trial
pyobfus input.py -o output.py --level pro

What's included in the trial:

• Control flow flattening (--control-flow)
• AES-256 string encryption (--string-encryption)
• Anti-debugging protection (--anti-debug)
• Dead code injection (--dead-code)
• License embedding (--expire, --bind-machine, --max-runs)
• Configuration presets (--preset trial/commercial/library/maximum)

After your trial, purchase a license to continue using Pro features.

The trial runs on the honor system. It stores its state in an unsigned file in your home directory, and pyobfus/trial.py is readable Apache-2.0 source — so it is a convenience control, not a security boundary, and we document it as such rather than claiming protection it cannot deliver. See SECURITY.md. Note that the Community Edition has no file or line limits and needs no trial at all — the trial gates only the Pro mechanisms.

Purchase Professional Edition

Pro Edition Features:

• 🔀 Control Flow Flattening
• 🧩 Dead Code Injection
• 🔐 AES-256 String Encryption
• 📦 Import Obfuscation - runtime importlib imports with encrypted import strings
• 🛡️ Anti-Debugging Checks
• 📅 License Embedding - Expiration, machine binding, run limits
• ⚡ Configuration Presets - One-command setup
• 🔄 Lifetime Updates
• 💻 Up to 3 devices per license
• 📧 Priority Email Support

Price: $45.00 USD (one-time payment)

Payment methods: credit/debit card, Apple Pay, and WeChat Pay (微信支付) for buyers in China, plus the other options Stripe shows for your region at checkout. Alipay (支付宝) is being enabled.

How to Purchase

Visit our purchase page: pyobfus.github.io/purchase for detailed information and secure checkout.

Quick purchase: 🚀 Buy Now - Direct checkout link (Instant delivery • 30-day money-back guarantee)

3-Step Purchase Process:

1. Complete Secure Checkout (Stripe)

   • Click the buy link above or visit the purchase page
   • Enter your email (for license delivery)
   • Complete payment securely via Stripe

2. Receive License Key

   • License key delivered to your email within minutes
   • Format: PYOB-XXXX-XXXX-XXXX-XXXX
   • Check Spam/Junk folder if not in inbox

3. Activate License

   Terminal

   Copy

   pip install --upgrade pyobfus
   pyobfus-license register PYOB-XXXX-XXXX-XXXX-XXXX
   pyobfus-license status
   

4. Start Using Pro Features

   bash

   Copy

   # Quick start with presets
   pyobfus src/ -o dist/ --preset commercial   # Maximum protection
   pyobfus src/ -o dist/ --preset trial        # 30-day trial version
   pyobfus src/ -o dist/ --preset library      # For pip distribution
   
   # Individual features
   pyobfus input.py -o output.py --string-encryption
   pyobfus input.py -o output.py --import-obfuscation
   pyobfus input.py -o output.py --anti-debug
   pyobfus input.py -o output.py --control-flow
   pyobfus input.py -o output.py --dead-code
   
   # License restrictions
   pyobfus src/ -o dist/ --expire 2025-12-31 --bind-machine --max-runs 100
   
   # All Pro features
   pyobfus input.py -o output.py --string-encryption --import-obfuscation --anti-debug --control-flow --dead-code
   

Support: For license activation, billing, or account questions, email zhurong0525@gmail.com with your license key. For bug reports or usage questions, please open a GitHub issue or start a discussion — that way the answer is there for the next person who hits the same thing.

Legal & Policies

By purchasing pyobfus Professional Edition, you agree to our:

• Terms of Service & EULA - License agreement and usage terms
• Refund Policy - 30-day money-back guarantee, no questions asked
• Privacy Policy - GDPR compliant, we protect your data

Quick Start

Installation

From PyPI (recommended):

pip install pyobfus

From source (for development):

git clone https://github.com/zhurong2020/pyobfus.git
cd pyobfus
pip install -e .

Basic Usage

# Obfuscate a single file
pyobfus input.py -o output.py

# Obfuscate a directory (cross-file mode - default in v0.2.0+)
pyobfus src/ -o dist/

# Preview obfuscation without writing files (v0.2.0+)
pyobfus src/ -o dist/ --dry-run

# Machine-readable plan: effective config, included/excluded files, artifacts
pyobfus src/ -o dist/ --dry-run --json

# Write output, then compile every generated .py in memory (no import/execution)
pyobfus src/ -o dist/ --verify-syntax --json

# Legacy single-file mode (v0.2.0+)
pyobfus src/ -o dist/ --no-cross-file

# With configuration file
pyobfus src/ -o dist/ --config pyobfus.yaml

# Preserve parameter names for keyword arguments (v0.1.6+)
pyobfus src/ -o dist/ --preserve-param-names

# Verbose output with progress indicators (v0.2.0+)
pyobfus src/ -o dist/ --verbose

Example

Before obfuscation:

def calculate_risk(age, score):
    """Calculate risk factor."""
    risk_factor = 0.1
    if score > 100:
        risk_factor = 0.5
    return age * risk_factor

patient_age = 55
patient_score = 150
risk = calculate_risk(patient_age, patient_score)
print(f"Risk score: {risk}")

After obfuscation:

def I0(I1, I2):
    I3 = 0.1
    if I2 > 100:
        I3 = 0.5
    return I1 * I3
I4 = 55
I5 = 150
I6 = I0(I4, I5)
print(f'Risk score: {I6}')

Note: Variable names (I0, I1, etc.) may vary slightly depending on code structure, but functionality is preserved.

Configuration

Quick Start with Templates

Generate a configuration template for your project type:

# For Django projects
pyobfus --init-config django

# For Flask projects
pyobfus --init-config flask

# For Python libraries
pyobfus --init-config library

# For general projects
pyobfus --init-config general

This creates a pyobfus.yaml file with sensible defaults for your project type.

Validate Configuration

Check your configuration file for errors before use:

pyobfus --validate-config pyobfus.yaml

The validator checks for:

• YAML syntax errors
• Invalid configuration options
• Common typos (e.g., exclude_pattern -> exclude_patterns)
• Pro features used with community level

Auto-Discovery

When you run pyobfus without -c, it automatically searches for:

1. pyobfus.yaml
2. pyobfus.yml
3. .pyobfus.yaml
4. .pyobfus.yml

Manual Configuration

Create pyobfus.yaml:

obfuscation:
  level: community
  exclude_patterns:
    - "test_*.py"
    - "**/tests/**"
    - "__init__.py"
  exclude_names:
    - "logger"
    - "config"
    - "main"
  remove_docstrings: true
  remove_comments: true

exclude_names Behavior

The exclude_names option preserves specified names from being renamed during obfuscation:

obfuscation:
  exclude_names:
    - MyPublicClass      # Name preserved, but strings inside are still encoded
    - exported_function  # Name preserved for external callers

Important: exclude_names only affects name obfuscation, not string encoding:

# Original
SECRET_KEY = "admin-password-123"

# With exclude_names: [SECRET_KEY] and string_encoding: true
SECRET_KEY = _decode_str('YWRtaW4tcGFzc3dvcmQtMTIz')
# ✅ Name 'SECRET_KEY' is preserved
# ✅ String content is still encoded (Base64)

Use cases:

• Preserve names for public APIs that external code imports
• Keep class/function names for debugging while still protecting string content
• Maintain compatibility with external frameworks expecting specific names

File Filtering

Exclude patterns support glob syntax:

• test_*.py - Exclude files starting with "test_"
• **/tests/** - Exclude all files in "tests" directories
• **/__init__.py - Exclude all __init__.py files
• setup.py - Exclude specific files

See pyobfus.yaml.example for more configuration examples.

Architecture

pyobfus uses Python's ast module for syntax-aware transformations:

1. Parser: Parse Python source to AST
2. Analyzer: Build symbol table with scope analysis
3. Transformers: Apply obfuscation techniques (name mangling, string encoding, etc.)
4. Generator: Generate obfuscated Python code

This approach ensures:

• Syntactically correct output
• Proper handling of Python scoping rules
• Support for modern Python features (f-strings, walrus operator, etc.)

Development

Setup

git clone https://github.com/zhurong2020/pyobfus.git
cd pyobfus
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -e ".[dev]"

Testing

# Run unit tests
pytest tests/ -v

# With coverage
pytest tests/ -v --cov=pyobfus --cov-report=html

# Run integration tests
pytest integration_tests/ -v

Integration Testing Framework (v0.1.6+): Test pyobfus on real-world code without uploading to PyPI. See INTEGRATION_TESTING.md for details.

Code Quality

# Format code
black pyobfus/

# Type checking
mypy pyobfus/

# Linting
ruff check pyobfus/

Use Cases

Protecting Proprietary Algorithms

Obfuscate sensitive business logic before distributing Python applications.

Educational Purposes

Demonstrate code protection concepts and obfuscation techniques.

Intellectual Property Protection

Add an additional layer of protection for commercial Python software.

Limitations

Current Limitations

• Keyword Arguments (✅ Resolved in v0.1.6): By default, parameter names are obfuscated, which breaks keyword arguments. Solution: Use the --preserve-param-names flag to preserve parameter names while still obfuscating function bodies.

  Example:

  python

  Copy

  # Before obfuscation
  def process(data_path, output_dir):
      temp_file = data_path + ".tmp"
      return temp_file
  
  result = process(data_path='./data', output_dir='./output')  # ✅ Works
  
  # After obfuscation (default behavior)
  def I0(I1, I2):
      I3 = I1 + ".tmp"
      return I3
  
  result = process(data_path='./data', output_dir='./output')  # ❌ TypeError!
  
  # After obfuscation (with --preserve-param-names)
  def I0(data_path, output_dir):
      I3 = data_path + ".tmp"
      return I3
  
  result = I0(data_path='./data', output_dir='./output')  # ✅ Works!
  

  When to use --preserve-param-names:

  • Public API functions/libraries where keyword arguments are used by clients
  • Functions with many parameters where keyword arguments improve readability
  • Code that relies heavily on keyword-only arguments (def func(*, kwonly))

  Trade-off: Parameter names reveal some information about the function's interface, but function bodies and local variables are still fully obfuscated.

• Cross-file imports: ✅ Resolved in v0.2.0 with full cross-file obfuscation support

• Dynamic code: eval(), exec() with obfuscated code may require adjustments

• Debugging: Obfuscated code is harder to debug (by design)

• Performance: Some obfuscation techniques may impact runtime performance

Recommendations

• Test obfuscated code thoroughly before deployment
• Keep original source in version control
• Use configuration files for reproducible builds
• For public APIs, use --preserve-param-names to maintain keyword argument compatibility
• Consider combining with other protection methods (compilation, etc.)

Technical Details

• Python Support: 3.9, 3.10, 3.11, 3.12, 3.13, 3.14 — including free-threaded 3.14 builds (python3.14t, verified: full test suite + a real seal/scrub-traceback obfuscate→execute→decrypt round trip)
• Naming Scheme: Index-based (I0, I1, I2...) - simple and effective
• Architecture: Modular transformer pipeline with two-phase cross-file obfuscation
• Testing: 1,000+ tests, 90% coverage, multi-OS CI/CD (Python 3.9-3.14 × Ubuntu / macOS / Windows)

Frequently Asked Questions

Is pyobfus Right for Me?

Use pyobfus if you:

• Need to protect proprietary algorithms before distributing Python applications
• Want a tool that "just works" without DLL conflicts or native dependencies
• Prefer transparent pricing without hidden trial limitations
• Support open-source software with optional paid features

How do I obfuscate Python code?

# Install
pip install pyobfus

# Obfuscate a single file
pyobfus script.py -o script_obf.py

# Obfuscate an entire project
pyobfus src/ -o dist/

# Preview without writing files
pyobfus src/ -o dist/ --dry-run

# Preview a structured, non-applicable protection plan for an AI/CI consumer
pyobfus src/ -o dist/ --dry-run --json

--verify-syntax is an opt-in post-build check: it compiles generated Python source in memory, creates no __pycache__, and reports syntax_valid in JSON. It does not import or execute the project and is not a runtime compatibility guarantee.

How do I obfuscate Python before selling or delivering it?

Run pyobfus --check first, build into a separate output directory, and keep the optional mapping.json outside the customer artifact. Ship the transformed tree, then run your normal tests or packaging step against that exact output. The PyInstaller, compiled-packaging, and import-hook cookbooks cover common delivery formats.

How do I debug an obfuscated crash with an AI assistant?

Build with --save-mapping mapping.json. When a production traceback arrives, run pyobfus --unmap --trace error.log --mapping mapping.json; the restored identifiers can then be read by you, Claude Code, Cursor, Copilot, or another AI assistant without giving the customer your private mapping file.

Is there an MCP server for Python obfuscation?

Yes. uvx pyobfus-mcp exposes eight local tools for risk scanning, config generation, project protection, verification, preset guidance, and traceback mapping. Source paths are validated locally and pyobfus does not upload project code or require an API key.

Will my code still work after obfuscation?

pyobfus is designed to preserve program behavior for supported Python syntax and framework patterns, and its compatibility matrix is covered by automated tests. Obfuscation is still a source transformation: run your own test suite and verify the built artifact, especially when the project relies on dynamic imports, reflection, or generated code.

Does obfuscated code run slower?

Minimal impact:

• Name mangling: Zero runtime cost (just renamed identifiers)
• String encoding (Base64): ~0.1ms per string at startup
• String encryption (AES-256, Pro): ~0.5ms per string at startup

Can I obfuscate Django/Flask projects?

Yes! Use our built-in templates:

# Django
pyobfus --init-config django

# Flask
pyobfus --init-config flask

# Then run obfuscation
pyobfus src/ -o dist/ -c pyobfus.yaml

What Python versions are supported?

pyobfus supports Python 3.9 through 3.14. Build and test the obfuscated artifact with the Python version used in production; cross-interpreter portability can depend on syntax, dependencies, and enabled transformations.

PyArmor vs pyobfus: Which should I choose?

Choose pyobfus if: You want transparent pricing, open-source trust, and simpler deployment without native dependencies.

See our detailed comparison for more information.

Can I use pyobfus alongside PyArmor or Nuitka?

Yes — and for many projects this is the most cost-effective approach. Use pyobfus as your always-on default layer (every module gets AST mangling + mapping for AI-debug compatibility), then stack PyArmor Pro's bytecode encryption or Nuitka's native compilation on the small set of modules that genuinely need stronger protection. The comparison now also covers why bytecode encryption should be treated as a stronger speed bump, not as irreversible cryptographic protection for client-side Python. See Layered Deployment Strategy in COMPARISON.md for the full reasoning.

Can I ship a single-file executable, like with Nuitka?

Yes, at a fraction of Nuitka Commercial's cost: obfuscate first, then bundle the obfuscated output with the free PyInstaller. The two tools solve different problems (name mangling vs. bundling a Python interpreter into one file) and compose cleanly — see the PyInstaller Cookbook for a full worked example, including verification that the original identifier names never reach the compiled binary and that pyobfus --unmap still reverses a traceback captured from the bundled exe.

What if obfuscation breaks my code?

1. Use --dry-run to preview changes before writing files
2. Use --preserve-param-names if you rely on keyword arguments
3. Add exclusions in pyobfus.yaml for names that must stay unchanged
4. Report issues on GitHub - we fix bugs quickly!

Can obfuscated code be reversed?

Name mangling removes the original identifiers from the emitted source and raises the cost of analysis, but it is not cryptographically irreversible: a determined analyst may infer names and behavior from context. Keep the optional mapping file private when you need reliable reverse mapping. For stronger protection, use Pro features:

• AES-256 encryption for strings
• Anti-debugging checks to prevent analysis

Security Note: String Encryption Limitations

Important: String encryption (AES-256) is designed as a deterrent against casual reverse engineering, not as cryptographic security.

Because obfuscated code must decrypt strings at runtime, the encryption key is necessarily embedded in the output. A determined attacker with access to the obfuscated code can:

1. Locate the embedded key
2. Extract and decrypt all strings

This is a fundamental limitation of ALL client-side obfuscators (including PyArmor, Nuitka, etc.) - true cryptographic security would require server-side decryption, which is impractical for most use cases.

What string encryption DOES provide:

• ✅ Prevents casual strings or grep searches from revealing sensitive text
• ✅ Increases effort required for reverse engineering
• ✅ Deters non-technical users from extracting information
• ✅ Adds a layer of protection combined with other techniques

What string encryption does NOT provide:

• ❌ Protection against determined reverse engineers
• ❌ Cryptographic security for secrets (use environment variables or secret management instead)
• ❌ DRM-level protection

Recommendation: For sensitive credentials (API keys, passwords), use environment variables or external secret management systems rather than embedding them in code.

How is pyobfus different from Cython/Nuitka?

Choose pyobfus if: You need cross-platform .py files without compilation overhead.

Documentation

For Users

• Installation & Quick Start - Get started in minutes
• Configuration Guide - YAML configuration and file filtering
• Examples - Working code examples demonstrating features
• Use Cases - Real-world application scenarios

For Developers

• Project Structure - Codebase architecture and development workflow
• Contributing Guide - How to contribute code and documentation
• Current Plan - Current project status and priorities
• Changelog - Version history and release notes

Community & Support

• GitHub Issues - Bug reports and feature requests
• GitHub Discussions - Questions, ideas, and community help
• Security Policy - How to report security vulnerabilities

Legal & License

• Dual License Model (see LICENSE-NOTICE.md):
  • pyobfus (Core): Apache 2.0 - Free and open source
  • pyobfus_pro (Pro): Proprietary - Requires paid license

Support the Project

If you find pyobfus helpful, consider supporting its development:

Buy Me A Coffee

Your support helps maintain and improve pyobfus. Thank you!

Citation

If you use pyobfus in academic work or want to reference it, please cite the archived release. The concept DOI below always resolves to the latest version:

APA

Zhu, R. (2026). pyobfus: An AST-based Python obfuscator with reverse stack-trace mapping for AI-assisted development. Zenodo. https://doi.org/10.5281/zenodo.20846053

BibTeX

@software{zhu_pyobfus,
  author    = {Zhu, Rong},
  title     = {pyobfus: An AST-based Python obfuscator with reverse stack-trace mapping for AI-assisted development},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.20846053},
  url       = {https://doi.org/10.5281/zenodo.20846053}
}

Machine-readable metadata is in CITATION.cff (GitHub's "Cite this repository" widget reads it).

Acknowledgments

• Inspired by Opy's AST-based approach
• Clean room implementation - no code copying