🤖 AI Analysis

Final verdict: SAFE

The package is generally safe with low risks across all categories except metadata, which has some concerns. The incomplete maintainer information and a suspicious non-HTTPS link are noteworthy but do not strongly indicate malicious intent.

Low network, shell, obfuscation, and credential risks.
Metadata risk due to incomplete maintainer information and a suspicious link.

Per-check LLM notes

Network: Network calls are expected for client packages that interact with APIs, indicating legitimate communication with an external service.
Shell: No shell execution patterns detected, suggesting no risk of command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Suspicious non-HTTPS link and incomplete maintainer information suggest potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

is not None else httpx.Client(timeout=_defaulted_timeout, follow_redirects=follow_redirect
is not None else httpx.Client(timeout=_defaulted_timeout), timeout=_defaulted_
is not None else httpx.AsyncClient(timeout=_defaulted_timeout, follow_redirects=follow_redirect
is not None else httpx.AsyncClient(timeout=_defaulted_timeout), timeout=_defaulted_

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://my.test.proxy.example.com

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with BasisTheoryClient

Create a fully-functional mini-application using the BasisTheoryClient Python package that serves as a secure data management tool. This application should allow users to securely store and retrieve sensitive information such as API keys, database credentials, and other secrets. The application should also provide features to manage these secrets efficiently.

### Core Functionality:
1. **User Authentication:** Implement user authentication to ensure only authorized users can access the stored secrets. Utilize BasisTheoryClient's authentication methods to integrate with Basis Theory's security infrastructure.
2. **Secret Storage:** Users should be able to add new secrets to the system, specifying details like name, type (e.g., API key, password), and description. Use BasisTheoryClient to securely store these secrets.
3. **Retrieve Secrets:** Provide functionality for users to search and retrieve their stored secrets based on various criteria such as name, type, or date of creation.
4. **Secret Management:** Allow users to update or delete secrets as needed. Ensure that all operations are logged for auditing purposes.
5. **Encryption:** All secrets should be encrypted before being stored in the system, leveraging BasisTheoryClient's encryption capabilities to maintain data security.
6. **Security Compliance:** Ensure the application complies with relevant security standards and best practices, including regular updates and patches.

### Suggested Features:
- **Role-Based Access Control (RBAC):** Implement RBAC to restrict access based on user roles and permissions.
- **Audit Logs:** Maintain detailed logs of all secret management activities for compliance and security monitoring.
- **Scheduled Rotations:** Automate the rotation of secrets at regular intervals to enhance security.
- **Integration Capabilities:** Offer integration options with popular development tools and platforms, such as CI/CD pipelines, to streamline the use of secrets in automated processes.

### How to Utilize BasisTheoryClient:
- **Authentication:** Use BasisTheoryClient's authentication functions to authenticate users and generate tokens for secure access.
- **Storage & Retrieval:** Leverage BasisTheoryClient's methods for storing and retrieving secrets securely. Ensure that all data is encrypted both in transit and at rest.
- **Management Operations:** Implement BasisTheoryClient's APIs for updating and deleting secrets efficiently.
- **Encryption & Decryption:** Utilize BasisTheoryClient's encryption utilities to handle the encryption and decryption of secrets.

### Deliverables:
- A well-documented Python application.
- A README file explaining how to install, configure, and run the application.
- Unit tests covering all major functionalities.
- Integration tests for any external services used.
- Security documentation outlining how the application complies with security standards.