arelle-release

v2.41.4 suspicious
4.0
Medium Risk

An open source XBRL platform.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential code obfuscation and incomplete author metadata. While there is no direct evidence of malicious intent, these factors warrant further investigation.

  • Potential code obfuscation through base64 encoding
  • Incomplete author metadata and possibly new/inactive account
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: The shell execution patterns are likely related to clipboard operations and process monitoring on macOS, which might be benign depending on the package's functionality.
  • Obfuscation: The use of base64 decoding and encoding might indicate an attempt to obfuscate code, but it could also be used for legitimate purposes like data encryption.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.4/10)

◈ 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://arelle.org/arelle/documentation/
  • Detailed PyPI description (6729 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 1020 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 10 unique contributor(s) across 100 commits in Arelle/Arelle
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • one fb = base64.b64decode(base64input) ungzippedBytes = b""
  • b = base64.b64decode(b64data.encode("latin-1")) # rem
  • tcltkVersion=Tcl().eval('info patchlevel'), lxmlVe
  • .getvar("tcl_library"), Tcl().eval('info patchlevel')) if syslog is not None:
  • buf += zlib.decompress(compressedBytes) file.close()
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • else "python" os.system(f'/usr/bin/osascript -e \'tell app "Finder" to set frontmost
  • : p = subprocess.Popen(['pbpaste'], stdout=subprocess.PIPE)
  • : p = subprocess.Popen(['pbcopy'], stdin=subprocess.PIPE) a
  • cs return int(subprocess.getoutput("ps -p {0} -o rss".format(os.getpid())).rpartition('\n')[2])
  • pen' try: subprocess.Popen([command,self.webCache.cacheDir]) except:
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: arelle.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Arelle/Arelle appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 arelle-release 
Create a financial data analysis tool using the 'arelle-release' Python package, which is an open-source XBRL platform. Your goal is to build a user-friendly application that allows users to upload XBRL-formatted financial reports and analyze them for key financial metrics such as revenue, expenses, profit margins, and other relevant indicators. This tool will be particularly useful for investors, analysts, and finance professionals who need to quickly extract and interpret financial data from various sources.

### Key Features:
1. **File Upload**: Allow users to upload XBRL files directly through the app’s interface.
2. **Data Extraction**: Utilize 'arelle-release' to parse and extract financial data from uploaded XBRL files.
3. **Metric Calculation**: Automatically calculate essential financial metrics based on extracted data.
4. **Visualization**: Provide visual representations of the analyzed data through charts and graphs.
5. **Comparison Tool**: Enable users to compare multiple financial reports side-by-side.
6. **Export Options**: Offer options to export analyzed data and visualizations in various formats like CSV, PDF, or Excel.

### Implementation Steps:
1. **Setup Environment**: Install necessary Python packages including 'arelle-release'. Ensure you have the latest version compatible with your Python environment.
2. **User Interface**: Develop a simple web-based UI where users can upload their XBRL files. Use frameworks like Flask or Django for backend development and HTML/CSS/JavaScript for frontend.
3. **Integration with Arelle**: Integrate 'arelle-release' into your application to handle XBRL file parsing and data extraction tasks efficiently.
4. **Data Processing**: Implement logic to process extracted data, calculating key financial metrics such as revenue, expenses, net income, etc.
5. **Visualization**: Use libraries like Matplotlib or Plotly to create visualizations of the analyzed data.
6. **Comparative Analysis**: Add functionality allowing users to select multiple files for comparison, displaying differences in metrics over time or across different entities.
7. **Export Functionality**: Implement export options so users can save their analysis results in preferred formats.
8. **Testing & Documentation**: Thoroughly test all functionalities and document your code and usage instructions clearly.

This project not only leverages the powerful capabilities of 'arelle-release' but also provides practical value to users by simplifying complex financial data analysis processes.