Secrets Audit#

Scan any project directory for exposed secrets, hardcoded credentials, and sensitive data leaks. Produces a severity-ranked report with remediation steps.

Quick Start#

bash

# Full project scan
python3 scripts/scan_secrets.py /path/to/project

# Scan with git history check
python3 scripts/scan_secrets.py /path/to/project --git-history

# Scan specific file types only
python3 scripts/scan_secrets.py /path/to/project --extensions .py,.js,.ts,.env,.yml,.json

# JSON output for CI integration
python3 scripts/scan_secrets.py /path/to/project --format json

What Gets Detected#

High Severity#

• API keys (AWS, GCP, Azure, OpenAI, Stripe, etc.)
• Database connection strings with credentials
• Private keys (RSA, SSH, PGP)
• OAuth tokens and refresh tokens
• JWT secrets and signing keys
• Password fields with literal values

Medium Severity#

• .env files with populated secrets
• Config files with credentials (database.yml, settings.py, etc.)
• Hardcoded URLs with embedded auth (user:pass@host)
• Webhook URLs with tokens
• Generic high-entropy strings in assignment context

Low Severity#

• TODO/FIXME comments mentioning secrets
• Placeholder credentials (admin/admin, test/test)
• Example API keys in documentation
• Commented-out credentials

Ignored (False Positive Reduction)#

• Lock files (package-lock.json, yarn.lock, etc.)
• Binary files
• Minified JS/CSS
• Test fixtures clearly marked as fake
• node_modules, .git, vendor directories

Scan Output#

The scanner produces a structured report:

text

=== Secrets Audit Report ===
Project: /path/to/project
Scanned: 247 files | Skipped: 1,203 files
Time: 2.3s

--- HIGH SEVERITY (3 findings) ---

[H1] AWS Access Key ID
  File: src/config/aws.js:14
  Match: AKIA...EXAMPLE
  Context: const accessKey = "AKIA..."
  Fix: Move to environment variable AWS_ACCESS_KEY_ID

[H2] Database Password
  File: config/database.yml:8
  Match: password: "pr0duction_p@ss"
  Fix: Use DATABASE_URL env var or secrets manager

--- MEDIUM SEVERITY (5 findings) ---
...

--- SUMMARY ---
High: 3 | Medium: 5 | Low: 2 | Total: 10
Recommendation: Rotate all HIGH severity credentials immediately

Workflow#

1. Scan#

Run scripts/scan_secrets.py against the target directory. The script:

• Recursively walks the directory tree
• Skips binary files, lock files, and dependency directories
• Applies 40+ regex patterns from references/secret-patterns.md
• Calculates entropy for potential secrets
• Deduplicates findings

2. Review#

Present findings grouped by severity. For each finding:

• Show the file, line number, and surrounding context
• Explain what type of secret was found
• Assess whether it's a real secret or false positive

3. Remediate#

For each confirmed finding, provide specific remediation:

• Which environment variable to use
• How to add to .gitignore
• Whether the secret needs rotation (if committed to git)
• Example code showing the fix

4. Verify#

After remediation:

• Re-run the scan to confirm fixes
• Check git history if secrets were ever committed
• Recommend adding pre-commit hooks to prevent future leaks

Git History Scanning#

When --git-history flag is used, the script also checks:

• Deleted files that contained secrets
• Previous versions of files that had secrets removed
• Commits with "secret", "password", "key" in messages

Important: if a secret was ever committed to git, it must be rotated even if later removed — it exists in git history.

CI Integration#

The script returns exit codes for CI pipelines:

• 0 — No findings
• 1 — Low/medium findings only
• 2 — High severity findings (should block deployment)

JSON output ( --format json) can be parsed by CI tools for automated reporting.

Pre-commit Hook Setup#

After an audit, recommend setting up a pre-commit hook. See references/prevention-guide.md for hook installation and configuration.