aperturedb

v0.4.59 suspicious
5.0
Medium Risk

ApertureDB Python SDK

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential network and obfuscation concerns. While there are no clear signs of malicious activity, the unusual coding practices and insufficient error handling raise suspicion.

  • Moderate network risk due to improper error handling
  • High obfuscation risk due to base64 decoding and unusual formatting
Per-check LLM notes
  • Network: The network calls are likely for downloading images and videos, which is somewhat expected for a package with a name like 'aperturedb', possibly related to image or video processing. However, the lack of proper error handling and configuration checks could introduce risks.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 decoding on blobs and unusual formatting may indicate an attempt to obscure code logic.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The package shows low maintenance and authorship effort, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.6/10)

○ 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 (3977 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

  • 177 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in aperture-data/aperturedb-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • / self.http_session = requests.Session() if self.config.verify_hostname: if sel
  • ry: imgdata = requests.get(url) downloaded = True except re
  • se self.http_client = requests.Session( ) if "http_client" not in kwargs else kwargs["http_
  • ok=True) videodata = requests.get(url) if videodata.ok: fd = open(filename
  • latest_version = json.loads(requests.get( "https://pypi.org/pypi/aperturedb/json", timeout=1)
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • response_blob_array = [base64.b64decode( b) for b in json_response['blobs']]
  • es found in schema") def eval(self, ctx: "QueryContext", part: "CompValue" )
✓ 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: aperturedata.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aperture-data/aperturedb-python appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aperturedb 
Develop a mini-application named 'ApertureQueryTool' using the ApertureDB Python SDK. This tool will serve as a powerful data querying interface, allowing users to perform complex queries on their datasets efficiently. The application should have a user-friendly command-line interface (CLI) where users can input their queries and receive results in real-time. Here are the key steps and features you need to implement:

1. **Setup**: Begin by installing the ApertureDB Python SDK via pip. Ensure your development environment is set up correctly.
2. **Data Connection**: Allow users to connect to their ApertureDB instance through the CLI. Implement options for specifying the database URL, authentication tokens, and other necessary credentials.
3. **Query Interface**: Develop a feature-rich query interface where users can input SQL-like queries. The application should support various query types including SELECT, JOIN, WHERE clauses, and more.
4. **Result Display**: Upon execution of a query, display the results in a tabular format. Include options for formatting the output such as CSV export or pretty-printing tables.
5. **Error Handling**: Implement robust error handling to manage invalid queries, connection issues, and other common errors gracefully.
6. **Advanced Features**: Consider adding advanced features like auto-completion for queries, query history, and the ability to save and load frequently used queries.
7. **Documentation & Testing**: Write comprehensive documentation for the CLI commands and ensure thorough testing of all functionalities.

By following these steps, you'll create a versatile tool that leverages the power of ApertureDB's querying capabilities, making it easier for developers and data analysts to work with complex datasets.

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