apache-airflow-providers-edge3

v3.7.0 suspicious
5.0
Medium Risk

Provider package apache-airflow-providers-edge3 for Apache Airflow

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks, particularly concerning shell execution and network requests, which could potentially be exploited. However, there are no clear signs of malicious activity.

  • High shell risk due to potential code injection
  • Network requests indicating possible dependency on external APIs
Per-check LLM notes
  • Network: Network requests to external services may indicate dependency on external APIs, but without context, it's hard to determine legitimacy.
  • Shell: Executing commands with shell=True is risky as it can lead to code injection vulnerabilities if not properly sanitized.
  • Obfuscation: The usage of dynamic imports and patching techniques might indicate an attempt at obfuscation, but could also be part of normal functionality for dynamic loading or testing purposes.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has some minor red flags but no strong indicators of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: conftest.py
  • 22 test file(s) detected (e.g. integration_test.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-edg
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4477 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 142 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • Call] | None = mock_responses.requests.get(("GET", URL(flaky_service))) assert calls as
  • Call] | None = mock_responses.requests.get(("POST", URL(unreliable_service))) assert calls
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • try: module = __import__(module_path, fromlist=[func_name]) self.extended_sysinfo = getattr(module, fun
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • @mock.patch.object( __import__("airflow.providers.edge3.models.db", fromlist=["EdgeDBManager"]).EdgeDBManager, "upgradedb", ) @mock.patch.o
  • @mock.patch.object( __import__("airflow.providers.edge3.models.db", fromlist=["EdgeDBManager"]).EdgeDBManager, "create_db_from_orm", ) @moc
  • @mock.patch.object( __import__("airflow.providers.edge3.models.db", fromlist=["EdgeDBManager"]).EdgeDBManager, "get_current_revision", ) @m
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • Popen | Process """Can be subprocess.Popen (for the spawn path) or multiprocessing.Process (for the fork
  • try: process = subprocess.Popen( [ sys.executable,
  • by a string array shell=True, env=env, stderr=STDOUT,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow 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 apache-airflow-providers-edge3 
Create a mini-application using Apache Airflow along with the 'apache-airflow-providers-edge3' package to manage and automate the processing of data from an edge computing device. This application will serve as a bridge between an edge device and a central data processing system, ensuring efficient data transmission and processing. Here are the steps and features to include in your application:

1. **Data Collection**: Simulate data collection from an edge device (you can use mock data for this purpose). The data could represent sensor readings, IoT device statuses, or any other relevant edge-generated data.
2. **Data Transformation**: Implement a transformation task that processes the collected data. For instance, you might need to convert raw sensor readings into meaningful metrics, such as temperature or humidity levels.
3. **Data Transmission**: Use Apache Airflow to schedule and automate the transmission of processed data to a central server or cloud storage solution. Ensure that this process is reliable and secure.
4. **Error Handling & Logging**: Incorporate robust error handling and logging mechanisms within your tasks to monitor the status of each operation and identify issues promptly.
5. **Visualization**: Finally, implement a simple visualization component that displays key insights derived from the processed data. This could be a basic dashboard showing trends over time or anomalies detected in the data.

Utilize the 'apache-airflow-providers-edge3' package to streamline these operations, leveraging its capabilities to enhance the integration and management of edge computing resources within your workflow. Your goal is to create a fully functional, end-to-end solution that demonstrates the power of Apache Airflow in managing complex data pipelines involving edge devices.

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