Auditing Flask 3.0 Dependencies for OWASP Top 10 Vulnerabilities

Before we examine specific tools, consider why dependency auditing matters for Flask applications.

Flask relies on several key dependencies—Werkzeug for request handling, Jinja2 for templating, itsdangerous for sessions, and others like Click and Blinker. These components have had vulnerabilities over time. For example, Werkzeug versions before 3.0.2 had a denial-of-service issue in multipart form parsing (GHSA-m2qf-hxjv-5gp6). Unpatched transitive dependencies can expose your app to known exploits.

OWASP Top 10 2021’s A06: Vulnerable and Outdated Components directly addresses this risk. When we use third-party packages without checking for CVEs, we inherit their security issues. Tools like pip-audit, poetry audit, and safety help us identify these systematically.

We’ll walk through using them on a typical Flask 3.0 project, discuss their differences, and cover fixes and integration.

OWASP Top 10 2021: A06 Vulnerable Components in Flask Context

Here’s how A06 manifests in Flask apps:

Risk	Flask Example	Detection Approach
Unpatched deps	Werkzeug DoS (GHSA-m2qf-hxjv-5gp6)	OSV-based scanners like pip-audit
Session issues	itsdangerous flaws	Malicious package checks (safety)
Template vulns	Jinja2 injection	CVE databases (all tools)

Flask 3.0 core deps include Werkzeug ^3.0, Jinja2 ^3.1, Click ^8.0, itsdangerous ^2.1, Blinker, MarkupSafe. Transitives add more risk surface.

Tools Setup: pip-audit vs poetry audit vs safety for Flask 3.0

# pip-audit (PyPA ref)
$ pip install pip-audit

# poetry audit (1.8+)
$ poetry self add poetry-plugin-security  # Native now

# safety CLI
$ pip install safety
$ safety auth  # API key for full DB

Each tool draws from different vulnerability databases and suits different workflows:

• pip-audit (from PyPA): Uses OSV.dev and PyPI API. Works well with requirements.txt or pyproject.toml.
• poetry audit: Integrated with Poetry lockfiles for reproducible environments.
• safety CLI: Proprietary database plus malicious package detection; requires API key for full access.

Of course, coverage varies by database—pip-audit and poetry audit both leverage OSV, while safety adds unique signals. You may find differences in results across tools.

Setting Up a Sample Flask 3.0 Project

To demonstrate, let’s create a minimal Flask 3.0 project. You can use Poetry with pyproject.toml for lockfile reproducibility, or requirements.txt for pip.

With Poetry (pyproject.toml):

[tool.poetry.dependencies]
python = "^3.12"
flask = "^3.0.3"
gunicorn = "^22.0"
werkzeug = "^3.0.3"  # Explicit pin if audits flag transitive issues

[tool.poetry.group.dev.dependencies]
pytest = "*"

Run poetry install to generate poetry.lock, then poetry audit checks against the locked versions.

With pip (requirements.txt):

flask==3.0.3
gunicorn==22.0.0

pip install -r requirements.txt installs, then pip-audit -r requirements.txt.

Running the Audits

Now that we have a project, let’s run the tools. Start with pip-audit:

pip-audit pyproject.toml  # Scans deps and transitives
# Or: pip-audit -r requirements.txt

Example output for vulnerable Werkzeug <3.0.2:

→ werkzeug==3.0.1
  GHSA-m2qf-hxjv-5gp6 (HIGH)
  Denial-of-service via large multipart payloads.
  Fixed in: >=3.0.2

(Actual output format from pip-audit v2+.)

For false positives or known safe vulns: pip-audit --ignore-vuln GHSA-m2qf-hxjv-5gp6.

Try poetry audit: poetry audit (lockfile only).

Safety: safety check -r requirements.txt.

Fixing Vulnerabilities

Once a vulnerability appears, you have options. pip-audit can attempt auto-fixes, but review changes—they may introduce breaking updates.

pip-audit --fix  # Proposes upgrades; confirm before applying
# Or dry-run first:
pip-audit --dry-run

For Poetry:

poetry add werkzeug@^3.0.2  # Updates pyproject.toml and lock
poetry lock --no-update  # Regenerate lock without broader changes

Always test your app after updates, as semantic versioning isn’t always followed perfectly.

Integrating Audits into CI/CD

Automate checks to catch issues early, but tune for false positives.

GitHub Actions example (fail on high/critical):

- name: Security audit
  uses: pypa/gh-action-pip-audit@v1
  with:
    inputs: pyproject.toml
    args: --audit-level high

Pre-commit hook:

- repo: https://github.com/pypa/pip-audit
  rev: v2.10.0  # Pin to stable
  hooks:
    - id: pip-audit
      args: ["pyproject.toml", "--ignore-vuln", "known-safe"]

Alternatives: GitLab CI, Dependabot alerts, or Snyk. Start strict, then whitelist safe vulns.

Additional Best Practices

To maintain security over time:

• Pin transitives if needed: poetry show --tree reveals the full dep tree; pin flagged ones explicitly.
• Schedule audits: Add to cron or CI: pip-audit -r requirements.txt.
• Generate SBOMs: pip-audit -f cyclonedx-json > flask-app.sbom.json for compliance.
• Monitor continuously: Use GitHub Dependabot, GitLab security, or OSV-Scanner.

Of course, no tool catches everything—combine with code reviews and threat modeling.

With no vulnerabilities reported, you’ve addressed A06 risks for your Flask app—though regular audits remain important as new CVEs emerge.

Consider running these tools periodically in your workflow to stay secure.