aioratio

v0.11.0 suspicious
8.0
High Risk

Async Python client for the Ratio EV Charging cloud API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks related to network and shell execution, suggesting potential misuse. While there is no definitive evidence of malicious intent, the combination of high-risk factors makes it suspicious.

  • High network risk
  • Potential for executing arbitrary code
  • Base64 obfuscation without clear purpose
Per-check LLM notes
  • Network: Network calls involve creating a client and using session objects, which could potentially be used to send sensitive information.
  • Shell: Executing arbitrary code through subprocess.run is highly suspicious and can be indicative of malicious activity.
  • Obfuscation: The presence of base64 decoding without clear context suggests potential obfuscation or hiding of critical information.
  • Credentials: No clear patterns indicative of credential harvesting are present, but caution is advised due to the obfuscation.
  • Metadata: The author information is incomplete and the maintainer has limited activity, which raises some suspicion but does not definitively indicate malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 23 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (15295 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 256 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 36 commits in aaearon/aioratio
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • e(TOKENS_PATH) async with aiohttp.ClientSession() as s: client = RatioClient(email=email, password=p
  • old_access)}") async with aiohttp.ClientSession() as s: client = RatioClient(email=email, password=p
  • ist[str] = [] async with aiohttp.ClientSession() as s: auth = CognitoSrpAuth( email=ema
  • (TOKENS_PATH) async with aiohttp.ClientSession() as s: client = RatioClient(email=email, password=p
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • e. """ secret_block = base64.b64decode(secret_block_b64) msg = pool_name.encode() + user_id_for
  • tamp() secret_block = base64.b64decode(secret_block_b64) msg = ( self._device_g
  • None try: return base64.b64decode(value, validate=True).decode("utf-8") except (binascii.E
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • in bleak'\n" ) res = subprocess.run( [sys.executable, "-c", code], capture_outpu
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: iosharp.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aaearon/aioratio 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 aioratio 
Create a fully functional mini-application called 'EVChargeMonitor' that allows users to monitor their electric vehicle charging sessions through the Ratio EV Charging cloud API using the Python package 'aioratio'. This app should serve as both a user-friendly dashboard and a command-line interface (CLI). The application should include the following key features:

1. **User Authentication**: Implement user registration and login functionality to secure access to individual charging session data.
2. **Real-Time Charging Data**: Utilize 'aioratio' to fetch real-time charging data including current charge level, charging rate, estimated time of completion, and total cost.
3. **Historical Data Analysis**: Allow users to view historical charging data, such as past charging sessions, average charging times, and cost trends over time.
4. **Notifications**: Set up notifications for when charging sessions start, end, or if there are any issues detected during charging.
5. **Customizable Dashboard**: Provide a customizable dashboard where users can set preferences for data display, such as preferred units of measurement (e.g., kWh vs. miles).
6. **CLI Interface**: In addition to the web-based dashboard, provide a CLI tool that can be used to check charging status, manage notifications, and view historical data without needing a graphical interface.
7. **Data Export**: Enable users to export their charging session data into CSV or JSON format for personal record-keeping or analysis.
8. **Error Handling**: Ensure robust error handling is implemented to gracefully handle any issues that may arise from network connectivity or API response errors.

The application should utilize the 'aioratio' package to asynchronously interact with the Ratio EV Charging API, ensuring efficient and responsive performance. This project aims to showcase the capabilities of 'aioratio' while providing practical value to electric vehicle owners.

💬 Discussion Feed

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