Evaluating Python library safety comes down to a few key dimensions:

Check the source and provenance

• PyPI page: Look at download counts, release history, and whether the project links to a real GitHub/GitLab repo.
• Author/org reputation: Libraries maintained by well-known companies (Google, Meta, Microsoft, Palantir) or established OSS orgs carry more trust than anonymous accounts.
• Typosquatting: Be careful with names that look like popular packages (e.g., requets instead of requests). Always double-check the exact name before installing.

Assess activity and maintenance

• Last release date: A library with no releases in 2+ years may have unpatched vulnerabilities.
• Open issues and PRs: A large backlog of unaddressed security issues is a red flag.
• Bus factor: If it’s one person maintaining it with no activity, it’s a dependency risk.

Look at the dependency chain

• Run pip show <package> to see what it pulls in — a simple-looking library can bring in dozens of transitive dependencies, each a potential risk.
• Tools like pip-audit or Safety scan your installed packages against known CVE databases.

Scan for known vulnerabilities

• pip-audit (recommended): pip-audit -r requirements.txt — queries the OSV and PyPI Advisory databases.
• Snyk: integrates well into CI/CD and covers transitive deps.
• safety check: Another CLI option, though now requires a free account.
• GitHub Dependabot: Flags vulnerable versions in your requirements.txt automatically.

Inspect the code itself (for high-risk libs)

• For anything touching auth, crypto, networking, or file I/O, a quick scan of the source is worthwhile.
• Watch for: obfuscated code, suspicious exec()/eval() calls, unexpected network calls at import time, or install hooks in setup.py.

Check the license

• Verify the license is compatible with your project (MIT, Apache 2.0, BSD = generally fine; GPL may have copyleft implications in commercial software).

Use a lock file and pin versions

• pip-compile (from pip-tools) or poetry.lock ensure you’re always using the exact audited version, not a silently updated one.

Practical starting point for most projects

Use common sense and search for things like “<library> reviews and reputation”. Sometimes it flags the most critical aspects !

#pip install pip-audit

#pip-audit -r requirements.txt

For CI/CD (assuming a GitHub Actions + Snyk setup), combining Snyk’s SCA with pip-audit in the pipeline gives you both real-time scanning and audit trail coverage — which, btw, also maps well to SOC 2 CC6.x controls around dependency risk. 😉