norm-findings 0.4.1

A lean CLI tool for normalizing security scanner findings based on DefectDojo parsers.

norm-findings

A lean CLI tool for normalizing security scanner findings based on DefectDojo parsers.

This project provides a standalone Python package and a minimal Docker image to convert findings from O(100) security scanners into a normalized format.

Open Source Attribution

This project is based on the excellent work of the DefectDojo community. We leverage their parser logic while providing a lean, dependency-minimized execution environment. See the NOTICE file for more details.

Installation

The default installation includes the core CLI and all parser dependencies, providing full functionality out-of-the-box.

Standard (Core + Parsers)

pip install .

Optional: Server Support

If you need the REST API server, install the server extra:

pip install ".[server]"

Optional: Development

For running tests or contributing:

pip install ".[dev]"

Running Tests

Unit Tests

Verify the core installation and stubs:

pytest tests/test_cli.py

E2E Parser Verification (Development only)

To verify all 200+ parsers against real DefectDojo sample data:

1. Ensure the development dependencies are installed (pip install ".[dev]").
2. Run the updater to fetch sample data:

   python -m norm_findings.updater
   

3. Run the E2E tests:

   pytest tests/test_e2e.py
   

Usage

norm-findings supports 200+ security scanners and accepts parser names in a case-insensitive manner, with or without the "Parser" suffix.

CLI

The CLI supports all parsers generically - just specify the parser name (case-insensitive):

# Basic usage - parser names are case-insensitive
norm-findings convert --parser trivy --input-file trivy.json --output-file findings.json

# Works with any case
norm-findings convert --parser TRIVY --input-file trivy.json --output-file findings.json
norm-findings convert --parser Trivy --input-file trivy.json --output-file findings.json

# Works with or without "Parser" suffix
norm-findings convert --parser TrivyParser --input-file trivy.json --output-file findings.json
norm-findings convert --parser anchore_grype --input-file grype.json --output-file findings.json

# With custom test label
norm-findings convert --parser bandit --input-file bandit.json --output-file findings.json --test "My App v1.0"

# Print findings to console
norm-findings convert --parser semgrep --input-file semgrep.json --output-file findings.json --print

Available parsers (200+): trivy, anchore_grype, bandit, semgrep, snyk, checkmarx, fortify, zap, burp, aqua, blackduck, sonarqube, veracode, and many more. See parser_mapping.json for the complete list.

Docker

# Case-insensitive parser names work in Docker too
docker run -v $(pwd):/dojo -it ghcr.io/scribe-security/norm-findings:latest \
  convert --parser trivy --input-file /dojo/trivy.json --output-file /dojo/findings.json

docker run -v $(pwd):/dojo -it ghcr.io/scribe-security/norm-findings:latest \
  convert --parser ANCHORE_GRYPE --input-file /dojo/grype.json --output-file /dojo/findings.json

Using as a Library (API)

The recommended way to use norm-findings as a library is through the high-level API, which handles parser resolution automatically:

from norm_findings.api import parse_findings

# Parse findings - parser name is case-insensitive
findings = parse_findings(
    parser_name="trivy",  # Case-insensitive: "trivy", "TRIVY", "Trivy" all work
    input_file="trivy.json",
    test_label="My Application v1.0",
    output_file="findings.json"  # Optional: writes findings to file
)

# Process findings
for finding in findings:
    print(f"{finding.severity}: {finding.title}")
    if finding.description:
        print(f"  Description: {finding.description}")

More API Examples:

# Example 1: Parse without writing to file
findings = parse_findings(
    parser_name="anchore_grype",
    input_file="grype-results.json",
    test_label="Production Scan"
)
print(f"Found {len(findings)} vulnerabilities")

# Example 2: Case-insensitive parser names
findings = parse_findings(
    parser_name="BANDIT",  # Upper case works too
    input_file="bandit-output.json",
    test_label="Security Audit"
)

# Example 3: With or without "Parser" suffix
findings = parse_findings(
    parser_name="SemgrepParser",  # "Parser" suffix is optional
    input_file="semgrep.json",
    test_label="Code Analysis"
)

# Example 4: Error handling
try:
    findings = parse_findings(
        parser_name="my_scanner",
        input_file="results.json"
    )
except ValueError as e:
    print(f"Parser error: {e}")
    # Error message includes list of available parsers

Advanced: Direct Parser Usage

If you need more control, you can instantiate parsers directly:

from norm_findings.parsers.trivy.parser import TrivyParser
from norm_findings.stubs.models import Test

parser = TrivyParser()
test = Test(product="My Application")

with open("trivy.json", "rb") as f:
    findings = parser.get_findings(f, test)

for finding in findings:
    print(f"Found: {finding.title} ({finding.severity})")

Legacy Version

The original monkey-patched version of this tool is preserved in the legacy-monkeypatch branch and tagged as v1.x-legacy.

To use the legacy version:

git checkout v1.x-legacy

Automatic Updates

norm-findings includes a built-in updater that fetches the latest parsers and tests from DefectDojo:

python -m norm_findings.updater

Development

Workflow

1. Branching: Create a new branch for your feature or bugfix from main.
2. Syncing Parsers: Run the updater to ensure you have the latest DefectDojo parsers:

   python -m norm_findings.updater
   

3. Testing: Always run the test suite before pushing:

   pytest tests/test_cli.py
   pytest tests/test_e2e.py --ignore norm_findings/stubs/models.py
   

4. Pushing: Push your branch to GitHub and open a Pull Request.

Versioning

norm-findings uses setuptools-scm for automatic versioning.

• The version is automatically derived from the most recent Git tag.
• When working on local uncommitted changes, the version will include a .dev suffix and the current timestamp.
• The version is written to norm_findings/_version.py during the build process.

Releasing

Releases are automated via GitHub Actions and are triggered by pushing a version tag:

1. Create a tag: Create a semantic version tag starting with v (e.g., v1.1.0):

   git tag -a v1.1.0 -m "Release version 1.1.0"
   

2. Push the tag:

   git push origin v1.1.0
   

3. Automated Pipeline: The build workflow will automatically:
   • Run all tests.
   • Build the Python wheel and source distribution.
   • Publish to PyPI.
   • Build and push the Docker image to GHCR (tagged with the version and latest).

Automatic Parser Updates

A daily GitHub Action runs the updater.py logic. If new parsers or updates are detected in DefectDojo:

1. A new branch auto-update-parsers is created.
2. A Pull Request is opened with a summary of the changes.
3. Maintainers can review and merge the PR to keep norm-findings up-to-date.