ossatrisk 0.4.9

Tracking high-risk oss packages: abandoned, undermaintained, or CVE-exposed.

ossatrisk

ossatrisk surfaces widely-used open source packages with hidden risks, like abandoned maintenance, unpatched CVEs, or a single-maintainer bus factor.

Only packages with high impact and broad usage are flagged.

The goal isn’t to blame anyone, but to get a clear picture of the ecosystem and act constructively, through contributions, forks where necessary, and fixes that help everyone relying on the code.

• Starting with the PHP ecosystem. More ecosystems planned.
• Datasets are updated daily.

CLI usage

You can install ossatrisk via PyPI:

pip install ossatrisk

Scan your project

# Scan the current directory for risky PHP packages
ossatrisk scan --ecosystem php

Options:

• --ecosystem (required) – currently supports only php.
• --format – output format; text (default) or json. Use json for machine-readable output.

Package Risk Assessment Algorithm

This project evaluates the risk level of software packages based on several key factors, helping developers identify dependencies that may pose potential stability or security issues.

1. Package Selection

1. We fetch popular packages from public package registries (e.g., Packagist, PyPI, npm).
2. From these, we filter packages that have not had a release in the last 12 months, as older packages are generally more likely to have maintenance or security issues. Only these packages are considered for risk scoring.

2. Risk Scoring Algorithm

For each selected package, a risk score is computed where higher scores indicate higher risk. The algorithm considers multiple factors:

Factor	Description	Weight / Logic
Recency	Time since the latest release	Older releases are riskier. The risk increases exponentially with age. If the package has very few open issues, recency is weighted less. If the package has many open issues, recency is slightly increased.
Known Vulnerabilities	Number of publicly reported security vulnerabilities (CVEs, advisories)	Each vulnerability significantly increases the risk.
Maintainers	Number of maintainers contributing to the package	Packages with few maintainers are considered riskier. Risk decreases as the number of maintainers increases.
Popularity	Downloads or usage metrics	Highly used packages have larger potential impact if issues arise. Risk is scaled logarithmically with download counts or usage statistics.
Open Issues	Number of unresolved issues in the package repository	More open issues indicate potential instability or lack of maintenance.

3. Risk Score Calculation

The final risk score is calculated as:

risk_score = recency_risk * vulnerability_risk * maintainer_risk * popularity_risk * issues_risk

This approach ensures that packages which are older, have known vulnerabilities, few maintainers, widely used, and many unresolved issues receive higher scores, flagging them as higher-risk dependencies.

Contributing

Contributions welcome - especially:

• Improving the scoring logic
• Improving the project structure
• Adding new ecosystems

Stargazers over time

License

MIT