atlas-profiler

v0.0.2b1 suspicious
5.0
Medium Risk

Data profiling with spatial column type annotation.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential code obfuscation aimed at hiding logic or evading detection. While there are no immediate signs of malicious activities such as credential theft or shell execution, the high obfuscation risk warrants closer scrutiny.

  • High obfuscation risk
  • Potential attempt to hide code logic or evade detection
Per-check LLM notes
  • Network: The network calls appear to be using the 'requests' library for HTTP GET requests, which is common for fetching data from APIs like Nominatim. This does not necessarily indicate malicious activity.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed patterns suggest potential obfuscation, possibly to hide code logic or evade detection, but further analysis is needed to confirm malicious intent.
  • Credentials: No clear signs of credential harvesting are present in the provided snippet.
  • Metadata: The package shows no signs of typosquatting and has minimal suspicious links. The maintainer's single package history suggests a new or less active account.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • 6 test file(s) detected (e.g. test_core_geo_samples.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8964 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)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 33 commits in VIDA-NYU/atlas-profiler
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • alues) _nominatim_session = requests.Session() def nominatim_query(url, *, q): url = url.rstrip("/"
  • try: response = requests.get(url, stream=True, timeout=300) response.raise_fo
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • lf.device) self.model.eval() def _format_input(self, text: str) -> str: ""
✓ 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 score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://schema.org/latitude
  • Non-HTTPS external link: http://schema.org/longitude
✓ Git Repository History

Repository VIDA-NYU/atlas-profiler appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Eden Wu" 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 atlas-profiler 
Create a mini-application named 'GeoDataInspector' that leverages the 'atlas-profiler' Python package to provide advanced data profiling capabilities specifically tailored for datasets containing geographic information. The application should allow users to upload CSV files, which may include columns representing various types of spatial data such as latitude-longitude pairs, addresses, or postal codes. Once uploaded, the app will use 'atlas-profiler' to automatically detect and annotate these spatial columns, providing detailed statistics about each one, including but not limited to count of unique values, null counts, and spatial distribution analysis.

The main functionalities of 'GeoDataInspector' should include:
- User-friendly interface for file upload.
- Automatic detection and annotation of spatial columns using 'atlas-profiler'.
- Detailed profiling report generation for each spatial column detected, highlighting key metrics and visualizations.
- Option to export the profiling results in both HTML and PDF formats for easy sharing and documentation.
- Integration with popular mapping APIs (such as Mapbox or Google Maps API) to visualize the geographical distribution of the data points.

In addition to these core features, consider adding the following optional enhancements to enrich user experience:
- Interactive dashboard for exploring different aspects of the dataset.
- Support for real-time updates on the dashboard as new data is loaded.
- Advanced filtering options to focus on specific subsets of the data for more granular analysis.
- Integration with cloud storage services (e.g., AWS S3, Google Cloud Storage) for seamless data management.

Ensure that the application is well-documented and includes clear instructions on how to install dependencies, including 'atlas-profiler', and run the application locally or on a server. Provide examples of how to interpret the profiling reports and use the visualizations to gain insights into the spatial characteristics of the data.

💬 Discussion Feed

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