Secret Storage Strategy

Relevant source files

The following files were used as context for generating this wiki page:

• SECURITY_SUMMARY.md
• PHASE1_IMPLEMENTATION.md
• SYSTEM_EVALUATION.md
• packages/cli/src/storage/SecretStorage.ts
• packages/cli/src/__tests__/storage.test.ts

The Secret Storage Strategy in SecretForge AI is a local-first architecture designed to secure sensitive credentials using industry-standard encryption and reliable local storage. The system prioritizes a zero-trust model where data remains on the developer's machine, encrypted at rest, and is only decrypted on-demand for specific operations.

This strategy centers on a robust storage layer that integrates a SQLite database with an AES-256-GCM cryptographic provider. By combining persistent relational storage with authenticated encryption, SecretForge ensures confidentiality, integrity, and availability of secrets across different development environments and projects.

Sources: PHASE1_IMPLEMENTATION.md:12-15, PHASE1_IMPLEMENTATION.md:237-248, SECURITY_SUMMARY.md:65-72

Architecture and Data Flow

The storage architecture is divided into a modular directory structure that separates cryptographic logic from storage management. The primary interface for data persistence is the SecretStorage layer, which utilizes the better-sqlite3 driver for synchronous, local operations.

Data Flow for Secret Addition

When a secret is added, the plaintext value is passed to a CryptoProvider, which generates a random IV and encrypts the value using AES-256-GCM. The resulting ciphertext, along with metadata such as tags and environment labels, is then persisted to the SQLite database.

The flow ensures that plaintext values are never stored directly on disk.
Sources: PHASE1_IMPLEMENTATION.md:18-35, PHASE1_IMPLEMENTATION.md:79-88, SECURITY_SUMMARY.md:46-55

Database Schema

SecretForge uses a relational schema to manage secrets with strict constraints to ensure data integrity and prevent duplication across environments.

Secrets Table Definition

Field	Type	Constraints	Description
id	TEXT	PRIMARY KEY	Cryptographically secure unique identifier (UUID v4)
name	TEXT	NOT NULL	The human-readable name of the secret (e.g., STRIPE_KEY)
project	TEXT	NOT NULL	Identifier for the project the secret belongs to
environment	TEXT	CHECK (dev, staging, prod)	Target environment for the secret
tags	TEXT	DEFAULT '[]'	JSON array of strings for organizational filtering
value_encrypted	TEXT	NOT NULL	The Base64-encoded ciphertext
created_at	TEXT	NOT NULL	ISO timestamp of creation
updated_at	TEXT	NOT NULL	ISO timestamp of last modification

Note: A unique constraint is enforced on the combination of (name, project, environment).
Sources: PHASE1_IMPLEMENTATION.md:40-52, SECURITY_SUMMARY.md:104-108, packages/cli/src/tests/storage.test.ts:70-80

Indexing Strategy

To optimize performance for common CLI operations like listing and injecting secrets, the following indices are maintained:

• idx_project_env: Optimized for filtering secrets by project and specific environments.
• idx_name: Optimized for direct secret retrieval by key name.

Sources: PHASE1_IMPLEMENTATION.md:54-55

Cryptographic Integration

The storage strategy relies on the DefaultCryptoProvider to secure data before it reaches the database. This implementation adheres to NIST-approved standards.

Encryption Properties

• Algorithm: AES-256-GCM (Galois/Counter Mode).
• Key Management: Derived from the SECRETFORGE_ENCRYPTION_KEY environment variable.
• Initialization Vector (IV): A unique 12-byte random IV is generated for every encryption operation to prevent pattern detection.
• Integrity: A 128-bit authentication tag is stored alongside the ciphertext to verify that data has not been tampered with.

The relationship between storage and crypto providers allows for modular replacement of encryption logic (e.g., for future KMS integration).
Sources: SECURITY_SUMMARY.md:46-55, PHASE1_IMPLEMENTATION.md:61-75, SYSTEM_EVALUATION.md:65-75

Storage Operations

The SQLiteSecretStorage class implements the core CRUD logic required by the CLI.

Secret Identification

Secret IDs are generated using a cryptographically secure method to prevent prediction attacks. The format follows a sec_ prefix followed by a hex-encoded UUID.

// Secure ID Generation Example
private generateId(): string {
  const crypto = require('crypto');
  return `sec_${crypto.randomUUID().replace(/-/g, '')}`;
}

Sources: SECURITY_SUMMARY.md:100-108, SYSTEM_EVALUATION.md:165-170

Retrieval and Filtering

The storage layer supports complex filtering during retrieval to accommodate the sf list and sf export commands.

Feature	Description	Implementation Detail
Project Filtering	Limits results to a specific project.	SQL WHERE project = ?
Env Filtering	Filters by dev, staging, or prod.	SQL WHERE environment = ?
Tag Filtering	Filters by specific metadata tags.	Parses JSON array in tags column.
Uniqueness	Prevents duplicate names in the same env.	Enforced by SQLite UNIQUE constraint.

Sources: PHASE1_IMPLEMENTATION.md:95-98, packages/cli/src/tests/storage.test.ts:140-165

Security Safeguards

The storage strategy includes several safeguards to protect sensitive data at rest and during execution:

1. Memory Management: Decrypted values are only held in memory for the duration of the request and are not cached.
2. Key Isolation: The encryption key is never stored in the database or the .secretforge.json configuration file.
3. Sanitization: Input values are escaped (backslashes and quotes) before storage to prevent injection attacks during later exports or injections.
4. Audit Trail: Every secret record includes created_at and updated_at timestamps to provide a basic audit history of secret lifecycle events.

Sources: SECURITY_SUMMARY.md:65-72, SECURITY_SUMMARY.md:80-90, SYSTEM_EVALUATION.md:180-185

Conclusion

SecretForge AI's Secret Storage Strategy provides a secure, reliable, and high-performance foundation for local secret management. By utilizing SQLite for structured persistence and AES-256-GCM for authenticated encryption, the system achieves a 5/5 security rating for data protection. The architecture is designed to scale from local development to CI/CD workflows while maintaining a zero-trust posture.