AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to high metadata risk factors such as untraceable repository and new maintainer with no history, which raises concerns about potential supply-chain attacks.
- High metadata risk
- Untraceable repository
- New maintainer with no history
Per-check LLM notes
- Network: The presence of network calls is not inherently suspicious but should be assessed within the context of the package's intended functionality.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows several red flags including an untraceable repository, a new maintainer with no history, and incomplete author details.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (8588 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
5 type-annotated function signatures (partial)
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
": res = requests.get(url, headers=headers, timeout=5) else:e: res = requests.post(url, headers=headers, json=payload, timeout=5)
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
Email domain looks legitimate: authnull.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 authsec-llamaindex
Create a secure AI assistant utility that leverages the 'authsec-llamaindex' package to manage and retrieve secure delegation tokens for interacting with AI services. Your goal is to develop a command-line tool that allows users to authenticate securely and delegate their access to specific AI functionalities via LlamaIndex. This tool will streamline the process of obtaining temporary access tokens that can be used to interact with various AI services without exposing long-term credentials. ### Steps to Build the Utility: 1. **Setup Project Environment**: Initialize a new Python project and install the necessary packages including 'authsec-llamaindex'. 2. **Authentication Module**: Implement a module that handles user authentication. Users should be able to log in using their credentials, which are then securely managed. 3. **Token Retrieval**: Integrate the 'authsec-llamaindex' package to handle the secure retrieval of delegation tokens based on user permissions and the specific AI service they wish to access. 4. **CLI Interface**: Develop a simple CLI interface that guides users through the login process and provides options to request different types of AI service tokens. 5. **Security Measures**: Ensure that all sensitive information, such as credentials and tokens, are handled securely. Utilize best practices for encryption and data protection. 6. **Testing**: Thoroughly test the application to ensure that it works as expected and that security measures are effective. 7. **Documentation**: Provide clear documentation explaining how to use the utility, including setup instructions and examples of common use cases. ### Suggested Features: - **Multi-service Support**: Allow users to request tokens for multiple AI services from a single login session. - **Role-based Access Control (RBAC)**: Implement RBAC to ensure that users only receive tokens for the services and actions they are authorized to perform. - **Token Expiry and Revocation**: Tokens should have a limited lifespan and should be easily revocable if needed. - **Audit Logs**: Maintain logs of token requests and usage for auditing purposes. By following these steps and incorporating the suggested features, you'll create a robust and secure utility that simplifies the process of delegating access to AI services.
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