aura-connector

v0.4.1 suspicious
5.0
Medium Risk

Async, typed Python data connector for AuraDB and existing databases.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential signs of obfuscation, which could be used to conceal malicious activities. While there is no clear evidence of malicious intent or active credential harvesting, the combination of these factors raises suspicion.

  • High obfuscation risk
  • Recently created repository with low activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Obfuscation: The observed patterns suggest potential malicious obfuscation techniques, particularly the use of base64 and zlib for decoding which can be used to hide malicious code.
  • Credentials: No clear evidence of credential harvesting is present, but further investigation is needed to rule out stealthy credential exfiltration methods.
  • Metadata: The repository's recent creation and low activity suggest potential risks, but lack of direct evidence of malicious intent.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Ohswedd/aura-connector/tree/main/docs
  • Detailed PyPI description (18291 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 439 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 14 commits in Ohswedd/aura-connector
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • in value: return base64.b64decode(value["__bytes__"]) if issubclass(target, enum.Enum)
  • stub that does not parse. compile(text, args.out or "<aura-stub>", "exec") if args.out: Path(args.out).write_text(text,
  • is Union or origin is getattr(__import__("types"), "UnionType", object()): args = [a for a in get_arg
  • try: body = zlib.decompress(body) except zlib.error as exc: raise Au
✓ 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

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository created very recently: 3 day(s) ago (2026-06-04T12:58:42Z)

  • Repository created very recently: 3 day(s) ago (2026-06-04T12:58:42Z)
  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aura Maintainers" 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 aura-connector 
Create a real-time financial dashboard using the 'aura-connector' package in Python. This dashboard will fetch and display live stock market data from an AuraDB database. Your task is to develop a minimalistic yet powerful tool that any investor would find useful for tracking their portfolio in real-time.

Step-by-step guide:
1. Set up your development environment with Python installed and the 'aura-connector' package imported.
2. Connect to an AuraDB instance using 'aura-connector'. Ensure you handle connection parameters securely.
3. Design a simple user interface (UI) using a library like Tkinter or PyQt for desktop applications. Alternatively, use Flask for a web-based UI.
4. Implement asynchronous fetching of stock data directly from the AuraDB database using 'aura-connector'. Ensure the data retrieval is efficient and does not overload the database server.
5. Display the fetched data in your chosen UI. Include options to filter data based on stock symbols, date ranges, etc.
6. Add functionality to refresh the displayed data periodically without reloading the entire page or application.
7. Incorporate error handling to manage potential issues such as network errors, database connection failures, etc.
8. Enhance the application by adding visualizations of the stock data trends over time, possibly using Matplotlib or Plotly.
9. Test your application thoroughly under various conditions to ensure reliability and responsiveness.
10. Document your code and provide instructions on setting up the environment and running the application.

Suggested Features:
- User authentication to protect individual portfolios.
- Support for multiple currencies and conversion rates.
- Historical data comparison and analysis tools.
- Alerts for significant changes in stock prices.
- Integration with other financial APIs for broader data sources.

How to Utilize 'aura-connector':
- Use 'aura-connector' to establish a secure connection to the AuraDB instance where stock data is stored.
- Leverage its asynchronous capabilities to fetch data efficiently without blocking the main thread of your application.
- Take advantage of its typed nature to ensure data integrity and ease of debugging.

💬 Discussion Feed

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