AI Analysis
Final verdict: SUSPICIOUS
The package exhibits potential obfuscation techniques and has low maintainer activity, raising concerns about its purpose and legitimacy.
- Obfuscation risk due to dynamic imports
- Low maintainer activity and anonymous author
Per-check LLM notes
- Obfuscation: The code pattern suggests dynamic import which can be used for obfuscation to hide module names or for evading simple static analysis.
- Credentials: No clear evidence of credential harvesting is present based on the provided code snippet.
- Metadata: The package shows signs of being newly created with minimal maintainer activity and an anonymous author, raising concerns about its legitimacy.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
read( lambda: urllib.request.urlopen(f"http://127.0.0.1:{port}/").read() )
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
try: mod = __import__(modname, fromlist=["_reset"]) except ImportError: continue re
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: proton.me>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
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 agent-pinboard
Create a mini-application called 'FactFinder' using the Python package 'agent-pinboard'. FactFinder is designed to help users extract and manage information from various sources such as web searches, database queries, and API responses, storing these facts in a session-scoped fact graph. Users will be able to interact with the app through a simple command-line interface (CLI), asking questions about the extracted facts or requesting specific data points. Steps to build FactFinder: 1. Install the 'agent-pinboard' package and set up your development environment. 2. Define a series of 'tool' functions that can fetch information from different sources (e.g., search engines, databases, APIs). 3. Use the '@pin' decorator provided by 'agent-pinboard' to automatically extract relevant facts from the return values of these tools. 4. Implement a feature where the user can query the fact graph directly, using natural language or structured queries. 5. Integrate a simple CLI that allows users to input commands and see the results of their queries. 6. Ensure that the application maintains a session-scoped fact graph, meaning all facts are stored during the current session but cleared when the session ends. 7. Add error handling and user-friendly messages to enhance usability. 8. Test the application thoroughly with various inputs and scenarios to ensure reliability. Suggested Features: - Support for multiple data sources (web, databases, APIs) - Ability to filter and sort facts based on relevance or date - User authentication for personal fact graphs - Exporting fact graphs to JSON or CSV for further analysis - Integration with a visual graph viewer for better understanding of relationships between facts How 'agent-pinboard' is utilized: - The '@pin' decorator simplifies the process of extracting and storing facts from tool returns. - The built-in graph-read tools allow the LLM to navigate and query the fact graph efficiently. - Session management ensures that facts are only accessible within the current session, providing a clean slate for each use.