🤖 AI Analysis

Final verdict: SUSPICIOUS

The package contains high obfuscation risks due to the use of eval() and exec(), which could potentially allow for code injection. However, other risk factors are low, suggesting no immediate signs of malicious intent or supply-chain attack.

High obfuscation risk due to use of eval() and exec()
Incomplete maintainer profile

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no suspicious system command executions.
Obfuscation: The use of eval() and exec suggests potential for code injection and obfuscation, indicating a higher risk.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The maintainer has an incomplete profile and may be new or inactive, but no other suspicious activities are observed.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

uncomfortable with the use of eval() and exec, then you should provide a specialised subcla
pdate(context) return eval(expression, my_globals) def execute(self, script, conte
try: dt = eval(expr) if isinstance(dt, datetime):
repeat, duration = eval(expr) spec['event_definition']['expression'] = f

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sartography.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository sartography/SpiffWorkflow 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 SpiffWorkflow

Create a task management mini-app using the SpiffWorkflow Python package. This app will allow users to define workflows for their tasks, assign tasks to different users, and track the progress of these tasks through various stages. Here’s a detailed breakdown of what your app should include:

1. **User Interface**: Develop a simple yet intuitive user interface where users can log in and manage their workflows.
2. **Workflow Definition**: Users should be able to define their workflows by specifying tasks, conditions, and transitions between tasks. This definition should be based on BPMN (Business Process Model and Notation).
3. **Task Assignment**: Each task within a workflow can be assigned to one or more users. The app should support dynamic assignment based on predefined rules.
4. **Progress Tracking**: Implement functionality to track the progress of each workflow instance. Users should be able to see which tasks have been completed and which are pending.
5. **Notifications**: Integrate a notification system that alerts users when a task is assigned to them or when they need to take action.
6. **Reporting**: Provide basic reporting features to generate summaries of completed workflows and tasks.

**Utilizing SpiffWorkflow**:
- Use SpiffWorkflow’s core functionalities to parse and validate BPMN models.
- Leverage SpiffWorkflow’s task execution capabilities to run workflows and handle task assignments.
- Utilize SpiffWorkflow’s event handling mechanisms to trigger notifications.
- Employ SpiffWorkflow’s persistence layer to store and retrieve workflow instances and tasks.

This project aims to showcase the versatility of SpiffWorkflow in managing complex task flows while providing a practical solution for task management.