aioca

v2.1 suspicious
4.0
Medium Risk

Asynchronous Channel Access client for asyncio and Python using libca via ctypes

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network calls and shell execution practices, which could potentially be exploited. However, there are no clear signs of malicious intent.

  • Unusual network call to 'switcher.json'
  • Potential for code injection through shell execution
Per-check LLM notes
  • Network: The network call to 'switcher.json' is unusual and may indicate unexpected behavior or external control.
  • Shell: Executing subprocesses with Python scripts can be legitimate but raises concerns about potential code injection or version checking that could mask updates.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
  • Metadata: The author's information is sparse and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. import_in_different_thread.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (1496 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

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

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in DiamondLightSource/aioca
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • tcher.json" switcher_exists = requests.get(switcher_json).ok if not switcher_exists: print(
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ture def ioc(): process = subprocess.Popen( [ sys.executable, "-m",
  • ait_for_ioc(ioc) output = subprocess.check_output( [ sys.executable, str(Path(
  • oca", "--version"] assert subprocess.check_output(cmd).decode().strip() == __version__
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: diamond.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository DiamondLightSource/aioca 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 aioca 
Create a Python-based asynchronous monitoring tool for industrial automation systems using the 'aioca' library. This tool will allow users to monitor real-time data from various process variables in an industrial setting. The application should have the following core functionalities:

1. **Connection Management**: The user should be able to connect to multiple OPC UA servers or any compatible server that supports the Channel Access protocol. Ensure that the connection is managed asynchronously to handle multiple connections efficiently.
2. **Data Subscription**: Once connected, the application should subscribe to specific process variables (PVs) to receive real-time updates on their values. These PVs could represent temperature sensors, pressure gauges, or other critical measurements in an industrial environment.
3. **Data Visualization**: Integrate a simple graphical interface using a library like Matplotlib or Plotly to visualize the real-time data received from the subscribed PVs. Users should be able to see trends over time and set up alerts based on thresholds.
4. **Alert System**: Implement an alert system where users can set up conditions under which they are notified (via email, SMS, or a desktop notification). For example, if a temperature sensor exceeds a certain threshold, the user should receive an immediate alert.
5. **Configuration Interface**: Provide a configuration interface where users can add, remove, or modify subscriptions and alert conditions without needing to restart the application.
6. **Logging and Reporting**: Include logging of all events (e.g., connection status, data received, alerts triggered) and provide a feature to generate reports summarizing the logged data.

The 'aioca' package will be crucial in handling the asynchronous communication with the servers and managing the data streams efficiently. It provides a Pythonic interface to interact with the Channel Access protocol, which is widely used in industrial automation for real-time data acquisition. By leveraging 'aioca', you'll ensure that your application can scale well and handle high-frequency data streams effectively.

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