aceteam-workflow-engine

v2.0.0rc12 suspicious
4.0
Medium Risk

A powerful workflow orchestration engine for composing complex computational tasks from modular, type-safe nodes

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell execution behaviors that could potentially be misused. However, there is no concrete evidence of malicious intent.

  • network risk due to custom User-Agent tracking
  • shell risk from executing external commands
Per-check LLM notes
  • Network: The package makes network calls using a custom User-Agent which may be used to track installations or report usage.
  • Shell: Executing external commands like 'uv run' without user interaction could indicate the package performs actions not aligned with its declared purpose, possibly leading to unintended system modifications.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of malicious credential theft.
  • Metadata: The package shows some red flags such as an author with a missing name and a new or inactive account, but there's no clear evidence of typosquatting or malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • onkeypatch this.""" req = urllib.request.Request(url, headers={"User-Agent": "wengine-install"})
  • "wengine-install"}) with urllib.request.urlopen(req, timeout=30) as resp: return resp.read()
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • e modes. """ result = subprocess.run( ["uv", "run", "--project", str(root), "python", "-c
  • ledProcessError`. """ subprocess.run(["uv", *args], cwd=cwd, check=True) __all__ = [ "ENGIN
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 aceteam-workflow-engine 
Create a mini-application called 'DataPipelineBuilder' using the Python package 'aceteam-workflow-engine'. This application will serve as a simple yet powerful tool for building data processing pipelines. It should allow users to define their own workflows consisting of various data transformation steps, such as filtering, aggregating, and enriching data, and then execute these workflows on provided datasets.

Step 1: Define Workflow Nodes
- Users should be able to create custom nodes for different data processing tasks. Each node should have a unique name, description, and set of inputs/outputs.

Step 2: Construct Workflows
- Allow users to connect these nodes together to form a directed acyclic graph (DAG) representing a complete data processing pipeline.
- Provide a visual editor or a command-line interface to make it easier for users to design and modify their workflows.

Step 3: Execute Workflows
- Implement functionality to run the constructed workflows on given datasets.
- Ensure that the execution process respects the dependencies between nodes and handles errors gracefully.

Step 4: Monitor and Debug
- Include a monitoring system to track the progress of workflow execution in real-time.
- Provide debugging tools to help users identify and fix issues in their workflows.

Suggested Features:
- Support for multiple input/output formats (CSV, JSON, etc.)
- Pre-built nodes for common data transformations (e.g., filtering, aggregation)
- Integration with cloud storage services for large datasets
- Ability to schedule periodic workflow executions

Utilizing 'aceteam-workflow-engine':
- Use the 'aceteam-workflow-engine' package to manage the creation, validation, and execution of the workflow DAGs.
- Leverage its type-safe nodes feature to ensure that data flows correctly between different steps of the pipeline without unexpected type mismatches.
- Employ the package's orchestration capabilities to handle complex dependencies and parallelism within your workflows.