AI Analysis
Final verdict: SUSPICIOUS
The package shows potential signs of obfuscation due to the use of pickle for deserialization without validation, which could be exploited for arbitrary code execution. However, there is no clear evidence of credential harvesting or other malicious activities.
- Potential obfuscation risk due to insecure use of pickle for deserialization.
- Maintainer metadata is incomplete and possibly suspicious.
Per-check LLM notes
- Obfuscation: The use of pickle for deserialization without proper validation may indicate obfuscation or an attempt to execute arbitrary code.
- Credentials: No direct evidence of credential harvesting patterns was found.
- Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some concerns but not definitive evidence of malicious intent.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
e == "pickle": return pickle.loads(data) raise ValueError(f"Unknown serialization type: {r
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: ibm.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository IBM/OpenDsStar appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author 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 OpenDsStar
Create a mini-application called 'LangGraphExplorer' that leverages the Python package 'OpenDsStar' to explore and analyze data using DS-Star agents on top of a LangGraph database. This application will serve as a tool for users to query, visualize, and manipulate graph data efficiently. Steps: 1. Set up a basic Flask web framework for the front-end user interface and API endpoints. 2. Integrate the 'OpenDsStar' package to enable DS-Star agents to process queries and perform analyses on the graph data stored in a LangGraph database. 3. Implement functionality for users to upload their own graph data (in a supported format like CSV or JSON). 4. Develop a feature that allows users to run complex queries on the uploaded data, utilizing DS-Star agents to optimize performance and provide meaningful insights. 5. Add visualization tools within the application to display the results of these queries in an interactive manner. 6. Include a dashboard where users can view statistics about their graph data, such as node count, edge count, and common patterns. 7. Ensure the application is user-friendly, with clear instructions and feedback messages for all actions. 8. Test the application thoroughly to ensure it handles various types of input data correctly and provides accurate results. Suggested Features: - Support for multiple query languages to cater to different user preferences. - Real-time updates to visualizations as users adjust query parameters. - Export options for query results in formats like CSV or JSON. - User authentication to allow personalized data storage and analysis. - Integration with popular graph databases besides LangGraph for broader compatibility. How 'OpenDsStar' is Utilized: - Use 'OpenDsStar' to initialize DS-Star agents that interact with the LangGraph database. - Leverage the DS-Star agents to execute complex queries on the graph data, optimizing the search and providing efficient results. - Employ 'OpenDsStar' functionalities to enhance the application's ability to handle large datasets and provide real-time analytics.