🐍 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
, Pipfile.lock
, pyproject.toml
, Pipfile
, 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
,Pipfile.lock
,pyproject.toml
,Pipfile
, andrequirements.txt
files - External Resolver Integration: Leverages
uv
andpip-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):
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:
Then use it with Python:
# or directly if scripts are in PATH
⚡ From Crates.io (Rust Package)
If you have Rust installed:
💾 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
|
🔧 From Source
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-tools (widely compatible, Python-based)
Environment Requirements:
- Resolvers must be available in your current environment
- If using virtual environments, activate your venv before running PySentry:
- 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)
# Using installed binary
# Automatically detects project type (uv.lock, poetry.lock, Pipfile.lock, pyproject.toml, Pipfile, requirements.txt)
# Force specific resolver
# Include all dependencies (main + dev + optional)
# Filter by severity (only show high and critical)
# Output to JSON file
Advanced Usage
# Using uvx for comprehensive audit
# Check multiple vulnerability sources concurrently
# Generate markdown report
# Control CI exit codes - only fail on critical vulnerabilities
# Or with installed binary
# Ignore specific vulnerabilities
# Disable caching for CI environments
# Verbose output for debugging
Advanced Requirements.txt Usage
# Scan multiple requirements files
# Check only direct dependencies from requirements.txt
# Ensure resolver is available in your environment
# Debug requirements.txt resolution
# Use longer resolution cache TTL (48 hours)
# Clear resolution cache before scanning
CI/CD Integration Examples
# Development environment - only fail on critical vulnerabilities
# Staging environment - fail on high+ vulnerabilities
# Production deployment - strict security (fail on medium+, default)
# Generate markdown report for GitHub issues/PRs
# Comprehensive audit with all sources and full reporting
# CI environment with fresh resolution cache
# CI with resolution cache disabled
Pre-commit Integration
PySentry integrates seamlessly with pre-commit to automatically scan for vulnerabilities before commits.
Setup
Add PySentry to your .pre-commit-config.yaml
:
repos:
- repo: https://github.com/nyudenkov/pysentry
hooks:
- id: pysentry # default pysentry settings
Advanced Configuration
repos:
- repo: https://github.com/nyudenkov/pysentry
hooks:
- id: pysentry
args:
Installation Requirements
Pre-commit will automatically install PySentry, uv and pip-tools via PyPI.
Configuration
PySentry supports TOML-based configuration files for persistent settings management. Configuration files follow a hierarchical discovery pattern:
- Project-level:
.pysentry.toml
in current or parent directories - User-level:
~/.config/pysentry/config.toml
(Linux/macOS) - System-level:
/etc/pysentry/config.toml
(Unix systems)
Configuration File Example
= 1
[]
= "json"
= "medium"
= "high"
= "all"
= false
[]
= ["pypa", "osv"]
[]
= "uv"
= "pip-tools"
[]
= true
= 48
= 72
[]
= false
= false
= "auto"
[]
= ["CVE-2023-12345", "GHSA-xxxx-yyyy-zzzz"]
Environment Variables
Variable | Description | Example |
---|---|---|
PYSENTRY_CONFIG |
Override config file path | PYSENTRY_CONFIG=/path/to/config.toml |
PYSENTRY_NO_CONFIG |
Disable all config file loading | PYSENTRY_NO_CONFIG=1 |
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 |
--detailed |
Show full vulnerability descriptions instead of truncated | 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
# Disable only resolution caching (keep vulnerability cache)
# Set resolution cache TTL to 48 hours
# Clear resolution cache on startup (useful for CI)
# Custom cache directory
To manually clear all caches:
# Linux
# macOS
# Windows (PowerShell)
To clear only resolution cache:
# Linux
# macOS
# Windows (PowerShell)
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
Pipfile.lock Files
Full support for Pipenv lock files with exact version resolution:
- Exact Version Resolution: Scans exact dependency versions locked by Pipenv
- Lock-File Only Analysis: Relies purely on the lock file structure, no Pipfile parsing needed
- Complete Dependency Tree: Analyzes all resolved dependencies including transitive ones
- Dependency Classification: Distinguishes between default dependencies and development groups
Key Features:
- No external tools required
- Fast parsing with exact version information
- Handles Pipenv's dependency groups (default and develop)
- Perfect for Pipenv-managed projects with established lock files
Pipfile Files (External Resolution)
Support for Pipfile specification files using external dependency resolvers:
Key Features:
- Dependencies Resolution: Converts version constraints from Pipfile 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
- Dependency Groups: Supports both default packages and dev-packages sections
- Complex Constraint Handling: Supports version ranges, Git dependencies, and environment markers
Resolution Workflow:
- Detects
Pipfile
in your project (whenPipfile.lock
is not present) - Auto-detects available resolver (
uv
orpip-tools
) in current environment - Resolves version constraints to exact dependency versions
- Scans resolved dependencies for vulnerabilities
- Reports findings with dependency group classification
Note: When both Pipfile
and Pipfile.lock
are present, PySentry prioritizes the lock file for better accuracy. Consider using pipenv lock
to generate a lock file for the most precise vulnerability scanning.
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:
- Detects
requirements.txt
files in your project - Auto-detects available resolver (
uv
orpip-tools
) in current environment - Resolves version constraints to exact dependency versions
- Scans resolved dependencies for vulnerabilities
- Reports findings with direct vs. transitive classification
Environment Setup:
# Ensure resolver is available in your environment
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
orpip-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
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
orpip-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
Running Tests
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
# Or specify the path explicitly
Error: "No dependency resolver found" or "uv resolver not available"
# Install a supported resolver in your environment
# Verify resolver is available
# If using virtual environments, ensure resolver is installed there
Error: "Failed to resolve requirements"
# Check your requirements.txt syntax
# Try different resolver
# Ensure you're in correct environment
# Debug with verbose output
Error: "Failed to fetch vulnerability data"
# Check network connectivity
# Try with different or multiple sources
Slow requirements.txt resolution
# Use faster uv resolver instead of pip-tools
# Install uv for better performance (2-10x faster)
# Or use uvx for isolated execution
Requirements.txt files not being detected
# Ensure requirements.txt exists
# Specify path explicitly
# Include additional requirements files
# Check if higher-priority files exist (they take precedence)
Performance Issues
# Clear all caches and retry
# Clear only resolution cache (if vulnerability cache is working)
# Clear resolution cache via CLI
# Use verbose mode to identify bottlenecks
# Disable caching to isolate issues
Resolution Cache Issues
# Clear stale resolution cache after environment changes
# Disable resolution cache if causing issues
# Extend cache TTL for stable environments
# Check cache usage with verbose output
# Force fresh resolution (ignores cache)