pysentry-rs 0.3.0

Security vulnerability auditing tool for Python packages

🐍 PySentry

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A fast, reliable security vulnerability scanner for Python projects, written in Rust.

Overview

PySentry audits Python projects for known security vulnerabilities by analyzing dependency files (uv.lock, poetry.lock, pyproject.toml, requirements.txt) and cross-referencing them against multiple vulnerability databases. It provides comprehensive reporting with support for various output formats and filtering options.

Key Features

• Multiple Project Formats: Supports uv.lock, poetry.lock, pyproject.toml, and requirements.txt files
• External Resolver Integration: Leverages uv and pip-tools for accurate requirements.txt constraint solving
• Multiple Data Sources:
  • PyPA Advisory Database (default)
  • PyPI JSON API
  • OSV.dev (Open Source Vulnerabilities)
• Flexible Output for different workflows: Human-readable, JSON, SARIF, and Markdown formats
• Performance Focused:
  • Written in Rust for speed
  • Async/concurrent processing
  • Multi-tier intelligent caching (vulnerability data + resolved dependencies)
• Comprehensive Filtering:
  • Severity levels (low, medium, high, critical)
  • Dependency scopes (main only vs all [optional, dev, prod, etc] dependencies)
  • Direct vs. transitive dependencies
• Enterprise Ready: SARIF output for IDE/CI integration

Installation

Choose the installation method that works best for you:

⚡ Via uvx (Recommended for occasional use)

Run directly without installing (requires uv):

uvx pysentry-rs /path/to/project

This method:

• Runs the latest version without installation
• Automatically manages Python environment
• Perfect for CI/CD or occasional security audits
• No need to manage package versions or updates

📦 From PyPI (Python Package)

For Python 3.8+ on Linux and macOS:

pip install pysentry-rs

Then use it with Python:

python -m pysentry /path/to/project
# or directly if scripts are in PATH
pysentry-rs /path/to/project

⚡ From Crates.io (Rust Package)

If you have Rust installed:

cargo install pysentry

💾 From GitHub Releases (Pre-built Binaries)

Download the latest release for your platform:

• Linux x64: pysentry-linux-x64.tar.gz
• Linux x64 (musl): pysentry-linux-x64-musl.tar.gz
• Linux ARM64: pysentry-linux-arm64.tar.gz
• macOS x64: pysentry-macos-x64.tar.gz
• macOS ARM64: pysentry-macos-arm64.tar.gz
• Windows x64: pysentry-windows-x64.zip

# Example for Linux x64
curl -L https://github.com/nyudenkov/pysentry/releases/latest/download/pysentry-linux-x64.tar.gz | tar -xz
./pysentry-linux-x64/pysentry --help

🔧 From Source

git clone https://github.com/nyudenkov/pysentry
cd pysentry
cargo build --release

The binary will be available at target/release/pysentry.

Requirements

• For uvx: Python 3.8+ and uv installed (Linux/macOS only)
• For binaries: No additional dependencies
• For Python package: Python 3.8+ (Linux/macOS only)
• For Rust package and source: Rust 1.79+

Platform Support

Installation Method	Linux	macOS	Windows
uvx	✅	✅	❌
PyPI (pip)	✅	✅	❌
Crates.io (cargo)	✅	✅	✅
GitHub Releases	✅	✅	✅
From Source	✅	✅	✅

Note: Windows Python wheels are not available due to compilation complexity. Windows users should use the pre-built binary from GitHub releases, install via cargo and build from source.

CLI Command Names

• Rust binary: pysentry (when installed via cargo or binary releases)
• Python package: pysentry-rs (when installed via pip or uvx)

Both variants support identical functionality. The resolver tools (uv, pip-tools) must be available in your current environment regardless of which PySentry variant you use.

Requirements.txt Support Prerequisites

To scan requirements.txt files, PySentry requires an external dependency resolver to convert version constraints (e.g., flask>=2.0,<3.0) into exact versions for vulnerability scanning.

Install a supported resolver:

# uv (recommended - fastest, Rust-based)
pip install uv

# pip-tools (widely compatible, Python-based)
pip install pip-tools

Environment Requirements:

• Resolvers must be available in your current environment
• If using virtual environments, activate your venv before running PySentry:

  source venv/bin/activate  # Linux/macOS
  venv\Scripts\activate     # Windows
  pysentry /path/to/project
  

• Alternatively, install resolvers globally for system-wide availability

Auto-detection: PySentry automatically detects and prefers: uv > pip-tools. Without a resolver, only uv.lock and poetry.lock files can be scanned.

Quick Start

Basic Usage

# Using uvx (recommended for occasional use)
uvx pysentry-rs
uvx pysentry-rs /path/to/python/project

# Using installed binary
pysentry
pysentry /path/to/python/project

# Automatically detects project type (uv.lock, poetry.lock, pyproject.toml, requirements.txt)
pysentry /path/to/project

# Force specific resolver
pysentry --resolver uv /path/to/project
pysentry --resolver pip-tools /path/to/project

# Include all dependencies (main + dev + optional)
pysentry --all-extras

# Filter by severity (only show high and critical)
pysentry --severity high

# Output to JSON file
pysentry --format json --output audit-results.json

Advanced Usage

# Using uvx for comprehensive audit
uvx pysentry-rs --all-extras --format sarif --output security-report.sarif

# Check multiple vulnerability sources concurrently
uvx pysentry-rs --sources pypa,osv,pypi /path/to/project
uvx pysentry-rs --sources pypa --sources osv --sources pypi

# Generate markdown report
uvx pysentry-rs --format markdown --output security-report.md

# Control CI exit codes - only fail on critical vulnerabilities
uvx pysentry-rs --fail-on critical

# Or with installed binary
pysentry --all-extras --format sarif --output security-report.sarif
pysentry --sources pypa,osv --direct-only
pysentry --format markdown --output security-report.md

# Ignore specific vulnerabilities
pysentry --ignore CVE-2023-12345 --ignore GHSA-xxxx-yyyy-zzzz

# Disable caching for CI environments
pysentry --no-cache

# Verbose output for debugging
pysentry --verbose

Advanced Requirements.txt Usage

# Scan multiple requirements files
pysentry --requirements requirements.txt --requirements requirements-dev.txt

# Check only direct dependencies from requirements.txt
pysentry --direct-only --resolver uv

# Ensure resolver is available in your environment
source venv/bin/activate  # Activate your virtual environment first
pysentry /path/to/project

# Debug requirements.txt resolution
pysentry --verbose --resolver uv /path/to/project

# Use longer resolution cache TTL (48 hours)
pysentry --resolution-cache-ttl 48 /path/to/project

# Clear resolution cache before scanning
pysentry --clear-resolution-cache /path/to/project

CI/CD Integration Examples

# Development environment - only fail on critical vulnerabilities
pysentry --fail-on critical --format json --output security-report.json

# Staging environment - fail on high+ vulnerabilities
pysentry --fail-on high --sources pypa,osv --format sarif --output security.sarif

# Production deployment - strict security (fail on medium+, default)
pysentry --sources pypa,pypi,osv --format json --output prod-security.json

# Generate markdown report for GitHub issues/PRs
pysentry --format markdown --output SECURITY-REPORT.md

# Comprehensive audit with all sources and full reporting
pysentry --sources pypa,pypi,osv --all-extras --format json --fail-on low

# CI environment with fresh resolution cache
pysentry --clear-resolution-cache --sources pypa,osv --format sarif

# CI with resolution cache disabled
pysentry --no-resolution-cache --format json --output security-report.json

Configuration

Command Line Options

Option	Description	Default
--format	Output format: human, json, sarif, markdown	human
--severity	Minimum severity: low, medium, high, critical	low
--fail-on	Fail (exit non-zero) on vulnerabilities ≥ severity	medium
--sources	Vulnerability sources: pypa, pypi, osv (multiple)	pypa
--all-extras	Include all dependencies (main + dev + optional)	false
--direct-only	Check only direct dependencies	false
--ignore	Vulnerability IDs to ignore (repeatable)	[]
--output	Output file path	stdout
--no-cache	Disable all caching	false
--cache-dir	Custom cache directory	Platform-specific
--resolution-cache-ttl	Resolution cache TTL in hours	24
--no-resolution-cache	Disable resolution caching only	false
--clear-resolution-cache	Clear resolution cache on startup	false
--verbose	Enable verbose output	false
--quiet	Suppress non-error output	false
--resolver	Dependency resolver: auto, uv, pip-tools	auto
--requirements	Additional requirements files (repeatable)	[]

Cache Management

PySentry uses an intelligent multi-tier caching system for optimal performance:

Vulnerability Data Cache

• Location: {CACHE_DIR}/pysentry/vulnerability-db/
• Purpose: Caches vulnerability databases from PyPA, PyPI, OSV
• TTL: 24 hours (configurable per source)
• Benefits: Avoids redundant API calls and downloads

Resolution Cache

• Location: {CACHE_DIR}/pysentry/dependency-resolution/
• Purpose: Caches resolved dependencies from uv/pip-tools
• TTL: 24 hours (configurable via --resolution-cache-ttl)
• Benefits: Dramatically speeds up repeated scans of requirements.txt files
• Cache Key: Based on requirements content, resolver version, Python version, platform

Platform-Specific Cache Locations

• Linux: ~/.cache/pysentry/
• macOS: ~/Library/Caches/pysentry/
• Windows: %LOCALAPPDATA%\pysentry\

Finding Your Cache Location: Run with --verbose to see the actual cache directory path being used.

Cache Features

• Atomic Updates: Prevents cache corruption during concurrent access
• Custom Location: Use --cache-dir to specify alternative location
• Selective Clearing: Control caching behavior per cache type
• Content-based Invalidation: Automatic cache invalidation on content changes

Cache Control Examples

# Disable all caching
pysentry --no-cache

# Disable only resolution caching (keep vulnerability cache)
pysentry --no-resolution-cache

# Set resolution cache TTL to 48 hours
pysentry --resolution-cache-ttl 48

# Clear resolution cache on startup (useful for CI)
pysentry --clear-resolution-cache

# Custom cache directory
pysentry --cache-dir /tmp/my-pysentry-cache

To manually clear all caches:

# Linux
rm -rf ~/.cache/pysentry/

# macOS
rm -rf ~/Library/Caches/pysentry/

# Windows (PowerShell)
Remove-Item -Recurse -Force "$env:LOCALAPPDATA\pysentry"

To clear only resolution cache:

# Linux
rm -rf ~/.cache/pysentry/dependency-resolution/

# macOS
rm -rf ~/Library/Caches/pysentry/dependency-resolution/

# Windows (PowerShell)
Remove-Item -Recurse -Force "$env:LOCALAPPDATA\pysentry\dependency-resolution"

Supported Project Formats

uv.lock Files (Recommended)

PySentry has support for uv.lock files:

• Exact version resolution
• Complete dependency graph analysis
• Source tracking
• Dependency classification (main, dev, optional) including transitive dependencies

poetry.lock Files

Full support for Poetry lock files:

• Exact Version Resolution: Scans exact dependency versions locked by Poetry
• Lock-File Only Analysis: Relies purely on the lock file structure, no pyproject.toml parsing needed
• Complete Dependency Tree: Analyzes all resolved dependencies including transitive ones
• Dependency Classification: Distinguishes between main dependencies and optional groups (dev, test, etc.)
• Source Tracking: Supports PyPI registry, Git repositories, local paths, and direct URLs

Key Features:

• No external tools required
• Fast parsing with exact version information
• Handles Poetry's dependency groups and optional dependencies
• Perfect for Poetry-managed projects with established lock files

requirements.txt Files (External Resolution)

Advanced support for requirements.txt files using external dependency resolvers:

Key Features:

• Dependencies Resolution: Converts version constraints (e.g., flask>=2.0,<3.0) to exact versions using mature external tools
• Multiple Resolver Support:
  • uv: Rust-based resolver, extremely fast and reliable (recommended)
  • pip-tools: Python-based resolver using pip-compile, widely compatible
• Auto-detection: Automatically detects and uses the best available resolver in your environment
• Multiple File Support: Combines requirements.txt, requirements-dev.txt, requirements-test.txt, etc.
• Dependency Classification: Distinguishes between direct and transitive dependencies
• Isolated Execution: Resolvers run in temporary directories to prevent project pollution
• Complex Constraint Handling: Supports version ranges, extras, environment markers, and conflict resolution

Resolution Workflow:

1. Detects requirements.txt files in your project
2. Auto-detects available resolver (uv or pip-tools) in current environment
3. Resolves version constraints to exact dependency versions
4. Scans resolved dependencies for vulnerabilities
5. Reports findings with direct vs. transitive classification

Environment Setup:

# Ensure resolver is available in your environment
source venv/bin/activate      # Activate virtual environment
pip install uv               # Install preferred resolver
pysentry /path/to/project    # Run security scan

pyproject.toml Files (External Resolution)

Support for projects without lock files:

• Parses version constraints from pyproject.toml
• Resolver Required: Like requirements.txt, needs external resolvers (uv or pip-tools) to convert version constraints to exact versions for accurate vulnerability scanning
• Limited dependency graph information compared to lock files
• Works with both Poetry and PEP 621 formats

Vulnerability Data Sources

PyPA Advisory Database (Default)

• Comprehensive coverage of Python ecosystem
• Community-maintained vulnerability database
• Regular updates from security researchers

PyPI JSON API

• Official PyPI vulnerability data
• Real-time information
• Limited to packages hosted on PyPI

OSV.dev

• Cross-ecosystem vulnerability database
• Google-maintained infrastructure

Output Formats

Human-Readable (Default)

Most comfortable to read.

Markdown

GitHub-friendly format with structured sections and severity indicators. Perfect for documentation, GitHub issues, and security reports.

JSON

{
  "summary": {
    "total_dependencies": 245,
    "vulnerable_packages": 2,
    "total_vulnerabilities": 3,
    "by_severity": {
      "critical": 1,
      "high": 1,
      "medium": 1,
      "low": 0
    }
  },
  "vulnerabilities": [...]
}

SARIF (Static Analysis Results Interchange Format)

Compatible with GitHub Security tab, VS Code, and other security tools.

Performance

PySentry is designed for speed and efficiency:

• Concurrent Processing: Vulnerability data fetched in parallel from multiple sources
• Multi-tier Caching: Intelligent caching for both vulnerability data and resolved dependencies
• Efficient Matching: In-memory indexing for fast vulnerability lookups
• Streaming: Large databases processed without excessive memory usage

Resolution Cache Performance

The resolution cache provides dramatic performance improvements for requirements.txt files:

• First scan: Standard resolution time using uv or pip-tools
• Subsequent scans: Near-instantaneous when cache is fresh (>90% time savings)
• Cache invalidation: Automatic when requirements content, resolver, or environment changes
• Content-aware: Different cache entries for different Python versions and platforms

Requirements.txt Resolution Performance

PySentry leverages external resolvers with intelligent caching:

• uv resolver: 2-10x faster than pip-tools, handles large dependency trees efficiently
• pip-tools resolver: Reliable fallback, slower but widely compatible
• Isolated execution: Prevents project pollution while maintaining security
• Resolution caching: Eliminates repeated resolver calls for unchanged requirements

Development

Building from Source

git clone https://github.com/nyudenkov/pysentry
cd pysentry
cargo build --release

Running Tests

cargo test

Project Structure

src/
├── main.rs           # CLI interface
├── lib.rs            # Library API
├── cache/            # Caching system
├── dependency/       # Dependency scanning
├── output/           # Report generation
├── parsers/          # Project file parsers
├── providers/        # Vulnerability data sources
├── types.rs          # Core type definitions
└── vulnerability/    # Vulnerability matching

Troubleshooting

Common Issues

Error: "No lock file or pyproject.toml found"

# Ensure you're in a Python project directory
ls pyproject.toml uv.lock poetry.lock requirements.txt

# Or specify the path explicitly
pysentry /path/to/python/project

Error: "No dependency resolver found" or "uv resolver not available"

# Install a supported resolver in your environment
pip install uv           # Recommended - fastest
pip install pip-tools    # Alternative

# Verify resolver is available
uv --version
pip-compile --version

# If using virtual environments, ensure resolver is installed there
source venv/bin/activate
pip install uv
pysentry /path/to/project

Error: "Failed to resolve requirements"

# Check your requirements.txt syntax
cat requirements.txt

# Try different resolver
pysentry --resolver pip-tools  # if uv fails
pysentry --resolver uv         # if pip-tools fails

# Ensure you're in correct environment
which python
which uv  # or which pip-compile

# Debug with verbose output
pysentry --verbose /path/to/project

Error: "Failed to fetch vulnerability data"

# Check network connectivity
curl -I https://osv-vulnerabilities.storage.googleapis.com/

# Try with different or multiple sources
pysentry --sources pypi
pysentry --sources pypa,osv

Slow requirements.txt resolution

# Use faster uv resolver instead of pip-tools
pysentry --resolver uv

# Install uv for better performance (2-10x faster)
pip install uv

# Or use uvx for isolated execution
uvx pysentry-rs --resolver uv /path/to/project

Requirements.txt files not being detected

# Ensure requirements.txt exists
ls requirements.txt

# Specify path explicitly
pysentry /path/to/python/project

# Include additional requirements files
pysentry --requirements requirements-dev.txt --requirements requirements-test.txt

# Check if higher-priority files exist (they take precedence)
ls uv.lock poetry.lock pyproject.toml

Performance Issues

# Clear all caches and retry
rm -rf ~/.cache/pysentry      # Linux
rm -rf ~/Library/Caches/pysentry  # macOS
pysentry

# Clear only resolution cache (if vulnerability cache is working)
rm -rf ~/.cache/pysentry/dependency-resolution/      # Linux
rm -rf ~/Library/Caches/pysentry/dependency-resolution/  # macOS
pysentry

# Clear resolution cache via CLI
pysentry --clear-resolution-cache

# Use verbose mode to identify bottlenecks
pysentry --verbose

# Disable caching to isolate issues
pysentry --no-cache

Resolution Cache Issues

# Clear stale resolution cache after environment changes
pysentry --clear-resolution-cache

# Disable resolution cache if causing issues
pysentry --no-resolution-cache

# Extend cache TTL for stable environments
pysentry --resolution-cache-ttl 168  # 1 week

# Check cache usage with verbose output
pysentry --verbose  # Shows cache hits/misses

# Force fresh resolution (ignores cache)
pysentry --clear-resolution-cache --no-resolution-cache

Acknowledgments

• Inspired by pip-audit and uv #9189 issue
• Originally was a command for uv
• Vulnerability data from PyPA, PyPI, and OSV.dev