ByteHide Secrets vs detect-secrets

ByteHide Secrets pairs secret scanning with centralized management for sensitive values. Its scanner searches source code, compiled binaries, repositories, and commit history, and can run locally or in CI/CD pipelines, including pull-request scanning. Listed targets range from API keys and cloud or database credentials to certificates, SSH keys, Kubernetes secrets, and infrastructure-as-code secrets. ByteHide says its machine-learning detection is intended to find secrets while limiting false positives; its documentation says scanning stays local and source code does not leave the user's environment.

For managing values, the product supports separate development, staging, and production settings, role-based access, audit logs, and SDK integration. ByteHide Vault provides encrypted, versioned storage, rollback, a local encrypted vault for isolated setups, cloud syncing, and scheduled credential rotation. Listed safeguards include encryption in transit and at rest, a zero-knowledge architecture, IP allowlisting, custom headers, and two-factor authentication. Integrations include GitHub, GitHub Actions, Azure DevOps, AWS, Jenkins, GitLab CI, Webpack, and Vite. It runs on API, Linux, macOS, self-hosted environments, web, and Windows. The free plan is free, with up to 2,000 secrets and no credit card required; a 14-day trial is also listed.

detect-secrets is a free, open-source Python module for finding secrets in codebases and helping stop new secrets from entering Git changes. It runs on Linux, macOS, and Windows, with self-hosted use documented through pip or Homebrew. Heuristic regular expressions inspect changes for newly committed secrets, alongside entropy and keyword detection. The scan command creates or updates a baseline of potential findings; hook flags new secrets; and audit lets analysts review, label, report, or compare baseline entries. Users can configure it as a pre-commit hook with the pre-commit framework. Built-in plugins and filters can be adjusted, and custom detector plugins and filters can be developed. Exclusions, matching-line or file exclusions, secret-value exclusions, and inline allowlist comments help handle false positives. The package can also be imported into Python scripts to scan files and serialize findings. Word-list filtering and gibberish detection require optional packages, and gibberish detection is disabled by default. The project uses Apache-2.0 licensing.