secrets-saver 0.1.0

A simple package to securely store and manage secrets.

Secrets Saver

A lightweight, FIPS 140-2/3 compliant Python library for securely storing and managing secrets.

SecretsSaver encrypts your data using AES-GCM and derives keys using PBKDF2HMAC (with SHA-256), provided by the standard cryptography library. It guarantees that users or applications are only prompted for the encryption/decryption key exactly once per instantiation.

It supports storing your encrypted secrets in:

• A local file (JSON based)
• A PostgreSQL database
• A Microsoft SQL Server (MSSQL) database

Features

• FIPS Compliant Cryptography: Uses AES-GCM for authenticated encryption and PBKDF2 with SHA-256 for secure key derivation.
• One-Time Prompting: Automatically prompts for the master key securely using getpass exactly once per instance via standard input (safely hiding the password).
• Flexible Storage: Works cleanly offline with local files but seamlessly transitions to PostgreSQL or MSSQL without changing how you interact with your secrets.
• Simple API: Easy to use dictionary-like interface masked behind set_secret and get_secret.

Installation

You will need the cryptography library. If you intend to use the database features, you will also need SQLAlchemy and the respective database drivers.

# For local file storage only
pip install cryptography

# For PostgreSQL support
pip install cryptography sqlalchemy psycopg2-binary

# For Microsoft SQL Server support
pip install cryptography sqlalchemy pyodbc

Usage

1. Local File Storage (Default)

from secrets_saver import SecretsSaver

# Will prompt for a key if secrets.db doesn't exist or upon first access
db = SecretsSaver("secrets.db")

# Store a secret
db.set_secret("api_token", "super_secret_value")

# Retrieve a secret
token = db.get_secret("api_token")
print(f"Retrieved token: {token}")

# List all stored keys
print(db.list_secrets())

2. PostgreSQL Storage

Pass a standard SQLAlchemy connection string via db_url.

from secrets_saver import SecretsSaver

db_url = "postgresql+psycopg2://admin:password@localhost:5432/my_database"
db = SecretsSaver(db_url=db_url)

db.set_secret("db_password", "my_postgres_secret")
print(db.get_secret("db_password"))

3. Microsoft SQL Server (MSSQL) Storage

Pass the MSSQL connection string. Be sure the ODBC driver specified matches what is installed on your system.

from secrets_saver import SecretsSaver

db_url = "mssql+pyodbc://admin:password@localhost/my_database?driver=ODBC+Driver+17+for+SQL+Server"
db = SecretsSaver(db_url=db_url)

db.set_secret("api_key", "my_mssql_secret")
print(db.get_secret("api_key"))

API Reference

SecretsSaver(filename="secrets.db", db_url=None)

Initializes the class. If db_url is provided, it attempts to connect to the SQL database using SQLAlchemy, automatically creating an encrypted_secrets table if it does not exist. Otherwise, it defaults to the local file filename.

• set_secret(key: str, value: str): Encrypts and saves a key-value pair to the database.
• get_secret(key: str) -> str: Decrypts and retrieves the value for the given key. Returns None if the key does not exist.
• list_secrets() -> list: Returns a list of all secret keys stored.
• clear_database(): Deletes all secrets in the current database/file and overwrites the storage with an empty encrypted payload.